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Prediction of storm surge and coastal inundation using Artificial Neural Network – A case study for 1999 Odisha Super Cyclone
Weather and Climate Extremes 23 (2019) 100196
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Prediction of storm surge and coastal inundation using Artificial Neural Network – A case study for 1999 Odisha Super Cyclone
T
Bishnupriya Sahoo, Prasad K. Bhaskaran∗ Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology Kharagpur, Kharagpur, 721 302, West Bengal, India
ARTICLE INFO
ABSTRACT
Keywords: Artificial Neural Network Odisha Super Cyclone Storm tide Coastal inundation
Tropical cyclone induced storm surge and associated onshore flooding poses significant danger and havoc to life, property and infrastructure during the time of landfall. Coastal belt along the East coast of India is thickly populated and also highly vulnerable to impact of tropical cyclones. Real-time forecasting system that provides reliable estimates on possible storm surge height, envelope and extent of onshore flooding has potential socioeconomic benefits. Conventional methods use state-of-art numerical models or ensemble of models that are computationally expensive and highly time consuming during real-time operations. This study proposes an alternate approach using soft computing techniques such as Artificial Neural Network (ANN) for the prediction of storm surge and onshore flooding. The proposed network architecture is proven to be viable and highly costeffective consistently maintaining high level of computational accuracy (> 92%) thereby finding potential realtime application. As a case study, the efficacy of ANN model in simulating storm-tide and extent of onshore flooding associated with the 1999 Odisha Super cyclone have been examined. Pre-computed scenarios of stormtide and inundation data were used to train ANN model for the entire Odisha coast with a success rate of 99%. After the training phase, computational time in prediction of storm surge and inundation is quite rapid (in order of seconds) as compared to any conventional model. Validation exercise performed to skill assess the robustness of ANN model using archived records of storm-tide and inundation obtained an accuracy of 92% and 94% respectively. Results obtained are quite encouraging demonstrating the efficacy of ANN model for real-time application and effectiveness for disaster risk reduction during tropical cyclone activity.
1. Introduction Coastal geomorphological features and funnel shaped characteristics of the head Bay region redirects about 68% of tropical cyclone disturbances that form over Bay of Bengal (BoB) to landfall along the East Coast of India (ECI). Based on historical records it is evident that about 4–5 cyclones develop over this region annually. In addition, the risk and coastal vulnerability associated with potential storm surge and onshore inundation is relatively higher along the East coast as compared to the West coast of India. Documentary evidences also indicate that the impact of tropical cyclones is relatively higher and devastating along ECI as compared to any other coast in the world. Additionally, the impact of sea level rise in the BoB region (average of 3.8 mm yr−1) is higher as compared to other global ocean basins (Rietbroek et al., 2016; Unnikrishnan et al., 2006). It acts as an additional threat and that can enhance the impact of coastal flooding in the low-lying deltaic environment surrounding the head Bay region as well other regions along the ECI.
∗
Amongst the four maritime states that adjoins the Bay of Bengal region, the State of Odisha is highly vulnerable and experiences the highest number of cyclone strikes. It is evident from historical archives that the 1999 Odisha Super Cyclone is the worst ever disaster recorded among tropical cyclones that formed over the Bay of Bengal. The loss of human life and destruction to property and infrastructural facilities that resulted from its aftermath is quite enormous. As mentioned above, the routine prediction of essential cyclone parameters such as cyclone track, probable landfall location, storm surge and extent of onshore flooding are disseminated in a realtime mode by weather agencies. It is also known that the forecast accuracy improves only when the cyclone is closer to the landfall location. In other words, the reliability in track forecast and probable landfall location is constrained only within 24 h before the landfall that is quite crucial for planning and evacuation operations. Also the numerical modeling activities required for a realtime operation involves executing ensemble of models that is highly time consuming and also demands high computational power. Therefore, it is worthwhile to explore an alternate approach that is
Corresponding author. E-mail addresses: [email protected], [email protected] (P.K. Bhaskaran).
https://doi.org/10.1016/j.wace.2019.100196 Received 15 September 2018; Received in revised form 2 November 2018; Accepted 13 January 2019 Available online 14 January 2019 2212-0947/ © 2019 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
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used ANN to predict hourly sea-level variation for diurnal, daily, 5daily, and 10-daily average sea levels. Bhaskaran et al. (2010) used ANN to forecast temperature and salinity in NIO basin. Kisi and Cimen (2012) used wavelet SVM (Support Vector Machines) to forecast precipitation. Barman et al. (2006), Bhaskaran and Pandey (2010), and Bhaskaran et al. (2008) used ANN to compute tsunami travel time for 250 coastal stations encompassing 35 different countries surrounding the Indian peninsula. Sztobryn (2003) used ANN to forecast storm surge for the Polish coast. You and Seo (2009) used ANN and cluster analysis to predict storm surge at various locations for the Korean Peninsula. Lee (2005, 2009) tested and validated neural network techniques for storm surge prediction for Taiwan coast. Application of GA in storm surge prediction was reported by You et al. (2012). In an unpublished work, the authors have investigated separately the performance of ANN, GA, GP and found the ability and accuracy of ANN to handle and forecast multiple objectives (outputs). ANN is used in the present study to classify and forecast storm surge and coastal inundation for the entire Odisha coast. ANN architecture is based on the structure of a human brain which comprises of weighted inputs, transfer functions, and outputs schematically represented by a set of equations that balances the corresponding inputs and outputs. The present study used a feed-forward ANN architecture based on supervised learning algorithm. In this case the model has supervised input learning and output corrected based on errors obtained in the previous iteration. Training and testing accuracy of ANN model for both storm surge height and extent of onshore inundation is obtained as 99% and 92% for the Odisha coast. As a case study, the ANN model was successfully tested for the 1999 Odisha Super cyclone, and the model skill level examined prior to the landfall event. The ability of ANN model to compute multiple outputs simultaneously also reduces the cost and time effectively, thereby resulting in a continuous prediction of storm-tide and coastal flooding all along the coast.
robust in terms of rapid computation as compared to conventional modeling techniques. The technique should be novel and also highly time efficient finding practical application for real-time operation. There are several analytical and numerical models developed in the past for real-time forecast, and more recent studies by Bhaskaran et al. (2013), Murty et al. (2016), and Gayathri et al. (2016) provides a comprehensive overview on the accuracy levels of storm surge and onshore inundation prediction using numerical models. It is evident that recent studies used the state-of-art ADCIRC (Advanced Circulation) model to compute the total water level elevation near-shore and associated onshore flooding in low-lying areas. Also for real-time operation both the atmospheric and hydrodynamic models as ensembles are used by the weather agency to forecast the probable landfall location, cyclone track and intensity, storm surge, and inundation characteristics. A novel approach was earlier reported for the Gulf coast, USA (Needham and Keim, 2012) that used historical dataset of storm surge and coastal inundation (SURGEDAT dataset) for decision support system and evacuation operation during emergency. It clearly demonstrates on the importance of pre-computed scenarios having potential application for emergency operations. However, such a study is unfortunately lacking at present for the Indian Ocean region. Most of the studies for tropical cyclone induced storm surge carried out for the Indian seas were case specific and performed for postlandfall cyclone events. In a recent study the authors developed a comprehensive dataset on pre-computed scenarios of storm-tide and coastal inundation maps for the Odisha State (Sahoo and Bhaskaran, 2017a) utilizing the ADCIRC (Advanced Circulation) model. This dataset is the most comprehensive dataset developed so far for the Indian coast. The pre-computed scenarios comprises information on storm-tide and onshore inundation associated with tropical cyclones based on various combinations of wind speeds, landfall locations, translation speed, angle of approach and tidal levels. The present study utilizes the pre-computed dataset (Sahoo and Bhaskaran, 2017a) to determine the best possible scenario of storm-tide and coastal inundation prior to the landfall of a cyclone using the Artificial Intelligence (AI) technique. In this context, the present study trains a suitable neural network architecture and demonstrates that this technique is highly cost and time effective thereby finding potential practical application in operational forecasting of tropical cyclone induced storm surge and associated flooding. The State of Odisha has a coastline length of about 480 km and a recent study by Sahoo and Bhaskaran (2017a) used the ADCIRC model to generate a comprehensive dataset of storm surge and coastal inundation with a resolution of 200 m for the entire coastline stretch of Odisha State. The logic behind choosing 200 dimensions is to demonstrate the ANN technique that was trained to forecast the storm surge and coastal inundation scenario for the Odisha coastline. In this case the 200 coastal points are separated approximately at every 2 km interval. The AI technique within the broad domain of computational intelligence is being widely recognized today due to its diverse practical application and beneficial value across multiple disciplines. AI techniques have grown attention in the field of ocean and atmospheric studies because of their proven robustness in data handling and accuracy in prediction. Branches of AI such as ANN, GA (Genetic Algorithm), GP (Genetic Programming) are efficiently used to classify and forecast parameters such as ocean waves, Sea Surface Temperature (SST), winds, rainfall, air temperature, tsunami travel time, and many more. Application that used ANN and GP are reported by Deo and Naidu (1999), Charate et al. (2009) for wave forecasting. More and Deo (2003) used this technique for wind forecasting. Makarynskyy (2004)
2. Data and methodology This section briefly describes the dataset used to train the ANN model and also demonstrates the building architecture used to predict storm-tide and associated onshore flooding. Pre-computed scenarios of storm-tide and inundation maps generated for the Odisha coast (Sahoo and Bhaskaran, 2017a) was used to train the ANN model. In another study, the authors (Sahoo and Bhaskaran, 2015) constructed multiple synthetic tracks or the most probable cyclone tracks at a spatial interval of every 40 km along the entire Odisha coast. Synthetic tracks along with relevant meteorological information for various combinations of wind speed, translation speed, angle of approach, and tidal amplitudes were provided as input to the ADCIRC model. This exercise resulted in generating 250 different possible scenarios of storm-tide and coastal inundation (more details are available in Sahoo and Bhaskaran, 2017a). Table 1 provides an overview on the different parameters and their combinations used to construct the comprehensive dataset. Fig. 1 shows the location of Odisha State, and the districts along the 480 km coastline. The Ganjam and Balasore districts are the respective southward/ northward boundaries of Odisha State. Fig. 1 deciphers the maximum tidal amplitude (in m) during the period October 19–25, 1999 associated with the 1999 Odisha Super cyclone from southern to northern boundary of Odisha Coast. The wide range of tide induced water level elevation along the Odisha coast attributed due to the variations in width and slope of the continental shelf off Odisha coast is pivotal modeling studies.
Table 1 List of Parameters used to generate the comprehensive dataset of storm tide and coastal inundation. Striking angle (in degrees)
Wind Speed (in knots)
Landfall Locations separated by 40 km interval alongshore the Odisha coast
Tidal conditions during landfall
40, 80, 90, 120
110, 120, 130, 140, 150
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
Spring Tide, Neap Tide
2
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Fig. 1. Maximum tidal amplitude (in m) during 1999 Odisha Super cyclone and locations along the coastal region of Odisha State.
hinder the learning process, while on the other hand specifying larger number of neurons can also result in over-training. The same holds good with the combination of transfer function in each layer and training function. This needs to be determined initially from trial and error method and that can provide confidence in using the optimum number. For training purpose more than 300 combinations of wind speed during landfall, landfall location (respective latitude and longitude), translation speed, incident angle of cyclone track w.r.t shoreline, and prevalent tidal conditions were used as input variables to ANN model. Similar number of multiple scenarios was also generated for storm tide and cross-shore inundation (generated using above combinations of input parameters) at 200 coastal destinations which being the output from the ANN model. A three-layered feed-forward ANN with two hidden layers and an output layer with multiple dimensions (200) are used in the present study. Tidal information at Paradeep Port is used to train the ANN model, as it is the only source of real time tidal information available for the Odisha coast. Based on trial and error method it is found that 25–30 neurons in each layer is the most ideal and would suffice the optimum network and that resulted in highest predictive accuracy level. Based on an unpublished work, the training functions trainlm (Levenberg-Marquardt) and trainbr (Bayesian Regulation) are found to provide the optimum performance. More mathematical details on the training functions used in the present study can be referred from Hagan and Menhaj (1994) and Foresee and Hagan (1997). Although, the present study signifies that the accuracy level of ANN model using the ‘trainbr’ training function is the highest, the simulation time is also quite high (approximately 3 h). Therefore, the ‘trainlim’ training function is found to be adequate for this study in context to both simulation time and accuracy. The overall network performed better for a combination of tan-sigmoid, log-sigmoid, and pure-linear transfer functions. The landfall location, approach angle, translation speed and wind speed were used as input to the ANN model and the output parameters are the storm tide and coastal inundation. Further, the ANN model was used to estimate the storm tide and coastal
Fig. 2. (a) Schematic representation of a single layer single output ANN model, (b) Network with multiple hidden layers for multi-dimensional output function.
The present study used a multi-layer, multi-output feed-forward ANN model trained with the pre-computed dataset of storm tide and onshore inundation. Fig. 2 provides a schematic illustration of the multi-layer, multi-output ANN model that shows the dependency of ANN on model parameters such as neuron number, transfer function, threshold function (training function) and also the input and output data for training. Specifying less number of neurons by the user can 3
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Fig. 3. Correlation coefficient between ADCIRC simulation and ANN prediction for (a) Storm tide (in m), (b) extent of coastal inundation (in km) during training and testing stages.
inundation for the 1999 Odisha Super Cyclone using the relevant input parameters obtained from archived record of tropical cyclones (presented in Table 1). The ANN predicted output of storm tide and cross shore inundation distance was further validated with the ADCIRC simulations, in situ observations and published records of storm tide and inundation extent that occurred during the Odisha Super cyclone.
parameters given in Table 2 are used as input to the ANN model to forecast the storm tide and coastal inundation. The performance of ANN model in terms of clock time is in the order of seconds and the model accuracy in predicting real-time surge and coastal flooding scenario is discussed in Section 3.1. 3.1. Validation of real-time storm tide and inundation using ANN model
3. Results and discussion
Fig. 4a represents a comparison between ANN predicted and ADCIRC simulated storm-tide during the Odisha Super cyclone surrounding the landfall location. Accuracy level obtained in the prediction of storm-tide is about 92% in terms of regression coefficient compared between ADCIRC simulation and ANN prediction (Fig. 5) for this extreme weather event. The computed maximum cross-shore inundation extent is about 50–60 km. Fig. 6a, b illustrates the comparison between ADCIRC and ANN predicted cross-shore extent of coastal inundation. The study reveals that the performance of ANN model in representing the extent of cross-shore inundation is highly satisfactory. In general, there is a marginal under-prediction in the crossshore inundation extent from ANN model for those locations far away from the cyclone landfall. Prediction accuracy level in terms of regression coefficient is about 94% (Fig. 7). The stormtide during this event predicted using ADCIRC and ANN model is depicted as a spatial plot in Fig. 4b. It clearly represents higher values of storm-tide for Jagatsingpur, Kendrapada, and Bhadrak which were the worst affected districts during this cyclone as Jagatsingpur district lies in the close vicinity of cyclone landfall, whereas Kendrapada and Bhadrak districts lies in the leeward side of the cyclone track. In addition, the presence of low-lying river delta (Sahoo and Bhaskaran, 2017b) has accelerated the storm-tide to create havoc in these districts in terms of severe coastal flooding. The spatial extent of onshore inundation shown in Fig. 6b covers the entire 480 km coastline stretch of Odisha. Inundation extent for most of the locations along southern boundary is less than 200 m. Locations centered along the left side of cyclone track within 150 km from landfall point experienced higher inundation ranging between 5.0 and 27 km. Locations in the right side of cyclone track experienced the highest onshore inundation (27 km–60 km) attributed due to strong winds and the presence of wide continental shelf. It is also evident that coastal inundation extends until the northern boundary of the state (Balasaore) which is around 150 km from the storm center. Depending on the wind speed strength and distribution, the southern belt of coastal Odisha was the least affected during 1999 Odisha Super cyclone. However, the wind speed along with the nature of coastal geomorphology created havoc in the central and the northern coast of Odisha State. Relatively steep continental shelf and topographic slope prevented the occurrence of higher surge height and wider coastal flooding in the Southern part,
Historical archives of tropical cyclone activity for the North Indian Ocean basin indicates that 1999 Odisha Super Cyclone was the worst ever-recorded cyclone that made landfall in Odisha State bordering the East coast of India. The devastation that resulted from this extreme weather event was quite severe in terms of enormous loss to life and property. The maximum wind speed during the time of landfall was about 260 km h−1. The storm surge spanned over 100–150 km stretch of coastline with a maximum surge exceeding 6 m found at the north of the landfall point (Rao et al., 2007). The study by Chittibabu et al. (2004) reported a surge of 7.5 m at Paradeep during this extreme event and that resulted in 9 885 catastrophic deaths and damage worth 2.8 billion USD. Based on post-storm survey and numerical modeling study, Dube et al. (2000) reported surge height exceeding 7 m at Paradeep during the 1999 Odisha Super Cyclone. However, the coastal inundation during this Super cyclone was mentioned as 50–60 km in various post-storm governmental and public reports and 2,00 000 ha of land was reported as infertile by saline intrusion. In the present study, ADCIRC model was used to map the storm tide and coastal inundation that occurred during 1999 Odisha Super Cyclone (Figure-4b, 6b) using the relevant track details from Joint Typhoon Warning Center (JTWC). However, this study lacks a detailed validation of the modeling exercise due to the unavailability of in situ measurements such as buoy deployment over this Indian coast. On the other hand, the skill level of ADCIRC model is very well proven based on simulations carried out for recent very severe cyclones, Phailin in 2013 by Murty et al. (2014). Also, the ADCIRC model showed a high level of confidence with the in situ observations for storm tide and coastal inundation at Paradeep during the 1999 Odisha cyclone (Sahoo and Bhaskaran, 2017a). Hence, the present study uses the ADCIRC simulations as benchmark to validate the ANN results. The ANN model was trained using the scenarios of pre-computed storm-tide and coastal inundation using the state-of-art ADCIRC model generated by Sahoo and Bhaskaran (2017a). Using the combination of training functions and transfer functions that are discussed in the methodology section, an accuracy level of 99% and 92% (Fig. 3) was obtained during the training and testing phase of the ANN model for storm tide and inundation extent respectively. The relevant cyclone 4
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Fig. 4. Comparison between ADCIRC simulated and ANN predicted Storm Tide (in m) surrounding the landfall location associated with 1999 Odisha Super cyclone (a) Axial representation (b) Spatial representation.
Fig. 5. Correlation coefficient for Storm tide (in m) between ADCIRC simulation and ANN prediction. 5
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Fig. 6. Comparison between ADCIRC simulated and ANN predicted Cross-shore Inundation Extent (in km) along the Odisha coast (a) Axial representation (b) Spatial representation. Table 2 Relevant parameters pertaining to 1999 Odisha Super Cyclone. Cyclone
Odisha Super Cyclone (October 1999) −1
Maximum wind speed (in m s ) during landfall Angle of approach (in degrees) Landfall location (Longitude, Latitude in decimal degrees) Translational speed (km h−1) Maximum water level (in m) recorded by tide gauge at Paradeep location Maximum Storm Surge (in m) Maximum Inundation along-shore direction (in km) Maximum Inundation cross-shore direction (in km)
however, the presence of major estuarine lows, shallow and wide continental shelf accelerated the surge to pile up and wide spread coastal flooding in the central and northern parts of Odisha coast. The generalized behavior of storm tide and coastal inundation extent along the diverse topography of Odisha coast can be referred from Sahoo and Bhaskaran (2017a). In case of both storm-tide and coastal inundation (Figs. 4b and 6b), ANN is found to perform marginal under-prediction. However, the overall analysis signifies that the ANN model reproduces the storm-tide
72 105 86.215° E, 20.16° N 14 1.65 6.0 100–150 50–60
and spatial extent of onshore inundation fairly well proving its applicability for real-time operations. Based on the skill level from ANN model it could be ascertained that this technique finds potential importance for real-time prediction of storm-tide and onshore inundation. It is also noted that the ANN model could preserve the observed trend for different locations in Odisha coast. It is therefore recommended that ANN model can be very useful in terms of rapid computation, preserving prediction accuracy, and thereby finds potential application for emergency preparedness and evacuation planning during extreme 6
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conducted under the CoE in Climate Change at IIT Kharagpur. References Barman, R., Prasad Kumar, B., Pandey, P.C., Dube, S.K., 2006. Tsunami travel time prediction using neural networks. Geophys. Res. Lett. 133, L16612. http://doi:10. 1029/2006GL026688. Bhaskaran, P.K., Nayak, S., SubbaReddy, B., Murty, P.L.N., Sen, D., 2013. Performance and validation of a coupled parallel ADCIRC-SWAN model for THANE cyclone in the Bay of Bengal. Environ. Fluid Mech. http://doi:10.1007/s10652-013-9284-5. Bhaskaran, P.K., Pandey, P.C., 2010. Tsunami early warning system: an Indian Ocean perspective. In: Natural and Anthropogenic Disasters: Vulnerability, Preparedness and Mitigation, pp. 100–125. http://doi:10.1007/978-90-481-2498-5_6. Bhaskaran, P.K., Rajesh Kumar, R., Barman, R., Ravichandran, M., 2010. A new approach for deriving temperature and salinity fields in the Indian Ocean using artificial neural networks. J. Mar. Sci. Technol. http://doi:10.1007/s00773-009-0081-2. Bhaskaran, P.K., Rajesh, K.R., Dube, S.K., Rao, A.D., Murty, T., Gangopadhyay, A., Chaudhuri, A., 2008. Tsunami early warning system - an Indian Ocean perspective. J. Earthquake Tsunami 2, 197–226. Charate, S.B., Deo, M.C., Londhe, S.N., 2009. Genetic programming for real time prediction of offshore wind. Int. J. Ships Offshore Struct TSOS 349431. http://doi:10. 1080/17445300802492638. Chittibabu, P., Dube, S.K., Macnabb, J.B., Murty, T.S., Rao, A.D., Mohanty, U.C., Sinha, P.C., 2004. Mitigation of flooding and cyclone hazard in Orissa, India. Nat. Hazards 31 (2), 455–485. http://doi:10.1023/B:NHAZ.0000023362.26409.22. Deo, M.C., Naidu, C.S., 1999. Real time wave forecasting using neural networks. Ocean Eng. 26, 191–203. http://doi:10.1016/S0029-8018(97)10025-7. Dube, S.K., Chittibabu, P., Rao, A.D., Sinha, P.C., Murty, T.S., 2000. Extreme sea levels associated with severe tropical cyclones hitting Orissa coast of India. Mar. Geodes. 23 (2), 75–90. https://doi.org/10.1080/01490410050030652. Foresee, F.D., Hagan, M.T., 1997. Gauss–Newton approximation to Bayesian regularization. In: Proceedings of the International Joint Conference on Neural Networks. IEEE Press, Piscataway, NJ, pp. 1930–1935. Gayathri, R., Murty, P.L.N., Bhaskaran, P.K., Srinivasa Kumar, T., 2016. A numerical study of hypothetical storm surge and coastal inundation for AILA cyclone in the Bay of Bengal. Environ. Fluid Mech. http://doi:10.1007/s10652-015-9434-z. Hagan, M.T., Menhaj, M., 1994. Training feed forward networks with the Marquardt algorithm. IEEE Trans. Neural Network. 5, 989–993. Kisi, O., Cimen, M., 2012. Precipitation forecasting by using wavelet-support vector machine conjunction model. Eng. Appl. Artif. Intell. 25, 783–792. http://doi:10. 1016/j.engappai.2011.11.003. Lee, T.L., 2005. Neural network prediction of a storm surge. Ocean Eng. 33, 483–494. http://doi:10.1016/j.oceaneng.2005.04.012. Lee, T.L., 2009. Predictions of typhoon storm surge in Taiwan using artificial neural networks. Advances in Engg. Software 40, 1200–1206. http://doi:10.1016/j. advengsoft.2007.06.005. Makarynskyy, O., 2004. Improving wave predictions with artificial neural networks. Ocean Eng. 31, 709–724. http://doi:10.1016/j.oceaneng.2003.05.003. More, A., Deo, M.C., 2003. Forecasting wind with neural networks. Mar. Struct. 16, 35–49. http://doi:10.1016/S0951-8339(02)00053-9. Murty, P.L.N., Bhaskaran, P.K., Gayathri, R., Sahoo, B., Srinivasa Kumar, T., SubbaReddy, B., 2016. Numerical study of coastal hydrodynamics using a coupled model for Hudhud cyclone in the Bay of Bengal. Estuar. Coast Shelf Sci. 183, 13–27. Murty, P.L.N., Sandhya, K.G., Bhaskaran, P.K., Jose, F., Gayathri, R., Balakrishnan Nair, T.M., Srinivasa Kumar, T., Shenoi, S.S.C., 2014. A coupled hydrodynamic modeling system for PHAILIN cyclone in the Bay of Bengal. Coast. Eng. 93, 71–81. Needham, H.F., Keim, B.D., 2012. A storm surge database for the US Gulf Coast. Int. J. Climatol. 32, 2108–2123. Rao, A.D., Dash, S., Babu, S.V., Jain, I., 2007. Numerical modeling of cyclone's impact on the ocean-A case study of the Orissa super cyclone. J. Coast Res. 23 (5), 1245–1250. Rietbroek, R., Brunnabend, S.E., Kusche, J., Schroter, J., Dahle, C., 2016. Revisiting the contemporary sea-level budget on global and regional scales. Proc. Natl. Acad. Sci. Unit. States Am. 113, 1504–1509. Sahoo, B., Bhaskaran, P.K., 2015. Assessment on historical cyclone tracks in the Bay of Bengal, east coast of India. Int. J. Climatol. http://doi:10.1002/joc.4331. Sahoo, B., Bhaskaran, P.K., 2017a. A comprehensive database for storm surge induced inundation and their climatic projection for the East coast of India. Int. J. Climatol. http://doi:10.1002/joc.5184. Sahoo, B., Bhaskaran, P.K., 2017b. Multi-hazard risk assessment of coastal vulnerability from tropical cyclones - a GIS based approach for the Odisha coast. J. Environ. Manag. 206, 1166–1178. https://doi.org/10.1016/j.jenvman.2017.10.075. Sztobryn, M., 2003. Forecast of storm surge by means of artificial neural network. J. Sea Res. 317–322. http://doi:10.1016/S1385-1101(03)00024-8. Unnikrishnan, A.S., Rupa Kumar, K., Fernandes, S.E., Michael, G.S., Patwardhan, S.K., 2006. Sea level changes along the Indian coast: observations and Projections. Curr. Sci. 90, 362–368. You, S.H., Lee, Y.H., Lee, W.J., 2012. Parameter estimations of a storm surge model using a genetic algorithm. Nat. Hazards 60, 1157–1165. http://doi:10.1007/s11069-0119900-y. You, S.H., Seo, J.W., 2009. Storm surge prediction using an artificial neural network model and cluster analysis. Nat. Hazards. http://doi:/10.1007/s11069-009-9396-x.
Fig. 7. Correlation coefficient for Cross-shore inundation (in km) between ADCIRC simulation and ANN prediction.
weather conditions. Overall the ANN model is much faster, robust and cost effective in comparison to conventional models used during realtime operation. 4. Summary and conclusions Accurate prediction of storm surge and associated coastal inundation during tropical cyclone landfall is an area of immense scientific interest having direct socio-economic implications. Though sophisticated numerical models were developed over time for real-time operations, an effective, robust and cost-effective tool is indispensable for timely warnings and planning operations. The present study reports on the application of a soft computing tool using Artificial Neural Network to predict the characteristics of storm-tide and associated inundation extent associated with 1999 Odisha Super cyclone as a case study. The ANN model was trained using a comprehensive dataset generated using ADICRC model simulated under various combinations of cyclone parameters for the entire coastline of Odisha State. The study clearly demonstrates the high predictive skill level of ANN model in comparison to conventional model and in situ data. A high level of correlation (92% for storm-tide and 94% for onshore inundation prediction) has been achieved from ANN model thereby recommending its applicability for real-time operations. Conflict of interest We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. Acknowledgments This study was conducted as a part of the Centre of Excellence (CoE) in Climate Change studies established at IIT Kharagpur funded by DST, Government of India. It forms a part of the project ‘Wind-Waves and Extreme Water Level Climate Projections for East Coast of India’
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Prediction of storm surge and coastal inundation using Artificial Neural Network – A case study for 1999 Odisha Super Cyclone
T
Bishnupriya Sahoo, Prasad K. Bhaskaran∗ Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology Kharagpur, Kharagpur, 721 302, West Bengal, India
ARTICLE INFO
ABSTRACT
Keywords: Artificial Neural Network Odisha Super Cyclone Storm tide Coastal inundation
Tropical cyclone induced storm surge and associated onshore flooding poses significant danger and havoc to life, property and infrastructure during the time of landfall. Coastal belt along the East coast of India is thickly populated and also highly vulnerable to impact of tropical cyclones. Real-time forecasting system that provides reliable estimates on possible storm surge height, envelope and extent of onshore flooding has potential socioeconomic benefits. Conventional methods use state-of-art numerical models or ensemble of models that are computationally expensive and highly time consuming during real-time operations. This study proposes an alternate approach using soft computing techniques such as Artificial Neural Network (ANN) for the prediction of storm surge and onshore flooding. The proposed network architecture is proven to be viable and highly costeffective consistently maintaining high level of computational accuracy (> 92%) thereby finding potential realtime application. As a case study, the efficacy of ANN model in simulating storm-tide and extent of onshore flooding associated with the 1999 Odisha Super cyclone have been examined. Pre-computed scenarios of stormtide and inundation data were used to train ANN model for the entire Odisha coast with a success rate of 99%. After the training phase, computational time in prediction of storm surge and inundation is quite rapid (in order of seconds) as compared to any conventional model. Validation exercise performed to skill assess the robustness of ANN model using archived records of storm-tide and inundation obtained an accuracy of 92% and 94% respectively. Results obtained are quite encouraging demonstrating the efficacy of ANN model for real-time application and effectiveness for disaster risk reduction during tropical cyclone activity.
1. Introduction Coastal geomorphological features and funnel shaped characteristics of the head Bay region redirects about 68% of tropical cyclone disturbances that form over Bay of Bengal (BoB) to landfall along the East Coast of India (ECI). Based on historical records it is evident that about 4–5 cyclones develop over this region annually. In addition, the risk and coastal vulnerability associated with potential storm surge and onshore inundation is relatively higher along the East coast as compared to the West coast of India. Documentary evidences also indicate that the impact of tropical cyclones is relatively higher and devastating along ECI as compared to any other coast in the world. Additionally, the impact of sea level rise in the BoB region (average of 3.8 mm yr−1) is higher as compared to other global ocean basins (Rietbroek et al., 2016; Unnikrishnan et al., 2006). It acts as an additional threat and that can enhance the impact of coastal flooding in the low-lying deltaic environment surrounding the head Bay region as well other regions along the ECI.
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Amongst the four maritime states that adjoins the Bay of Bengal region, the State of Odisha is highly vulnerable and experiences the highest number of cyclone strikes. It is evident from historical archives that the 1999 Odisha Super Cyclone is the worst ever disaster recorded among tropical cyclones that formed over the Bay of Bengal. The loss of human life and destruction to property and infrastructural facilities that resulted from its aftermath is quite enormous. As mentioned above, the routine prediction of essential cyclone parameters such as cyclone track, probable landfall location, storm surge and extent of onshore flooding are disseminated in a realtime mode by weather agencies. It is also known that the forecast accuracy improves only when the cyclone is closer to the landfall location. In other words, the reliability in track forecast and probable landfall location is constrained only within 24 h before the landfall that is quite crucial for planning and evacuation operations. Also the numerical modeling activities required for a realtime operation involves executing ensemble of models that is highly time consuming and also demands high computational power. Therefore, it is worthwhile to explore an alternate approach that is
Corresponding author. E-mail addresses: [email protected], [email protected] (P.K. Bhaskaran).
https://doi.org/10.1016/j.wace.2019.100196 Received 15 September 2018; Received in revised form 2 November 2018; Accepted 13 January 2019 Available online 14 January 2019 2212-0947/ © 2019 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
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used ANN to predict hourly sea-level variation for diurnal, daily, 5daily, and 10-daily average sea levels. Bhaskaran et al. (2010) used ANN to forecast temperature and salinity in NIO basin. Kisi and Cimen (2012) used wavelet SVM (Support Vector Machines) to forecast precipitation. Barman et al. (2006), Bhaskaran and Pandey (2010), and Bhaskaran et al. (2008) used ANN to compute tsunami travel time for 250 coastal stations encompassing 35 different countries surrounding the Indian peninsula. Sztobryn (2003) used ANN to forecast storm surge for the Polish coast. You and Seo (2009) used ANN and cluster analysis to predict storm surge at various locations for the Korean Peninsula. Lee (2005, 2009) tested and validated neural network techniques for storm surge prediction for Taiwan coast. Application of GA in storm surge prediction was reported by You et al. (2012). In an unpublished work, the authors have investigated separately the performance of ANN, GA, GP and found the ability and accuracy of ANN to handle and forecast multiple objectives (outputs). ANN is used in the present study to classify and forecast storm surge and coastal inundation for the entire Odisha coast. ANN architecture is based on the structure of a human brain which comprises of weighted inputs, transfer functions, and outputs schematically represented by a set of equations that balances the corresponding inputs and outputs. The present study used a feed-forward ANN architecture based on supervised learning algorithm. In this case the model has supervised input learning and output corrected based on errors obtained in the previous iteration. Training and testing accuracy of ANN model for both storm surge height and extent of onshore inundation is obtained as 99% and 92% for the Odisha coast. As a case study, the ANN model was successfully tested for the 1999 Odisha Super cyclone, and the model skill level examined prior to the landfall event. The ability of ANN model to compute multiple outputs simultaneously also reduces the cost and time effectively, thereby resulting in a continuous prediction of storm-tide and coastal flooding all along the coast.
robust in terms of rapid computation as compared to conventional modeling techniques. The technique should be novel and also highly time efficient finding practical application for real-time operation. There are several analytical and numerical models developed in the past for real-time forecast, and more recent studies by Bhaskaran et al. (2013), Murty et al. (2016), and Gayathri et al. (2016) provides a comprehensive overview on the accuracy levels of storm surge and onshore inundation prediction using numerical models. It is evident that recent studies used the state-of-art ADCIRC (Advanced Circulation) model to compute the total water level elevation near-shore and associated onshore flooding in low-lying areas. Also for real-time operation both the atmospheric and hydrodynamic models as ensembles are used by the weather agency to forecast the probable landfall location, cyclone track and intensity, storm surge, and inundation characteristics. A novel approach was earlier reported for the Gulf coast, USA (Needham and Keim, 2012) that used historical dataset of storm surge and coastal inundation (SURGEDAT dataset) for decision support system and evacuation operation during emergency. It clearly demonstrates on the importance of pre-computed scenarios having potential application for emergency operations. However, such a study is unfortunately lacking at present for the Indian Ocean region. Most of the studies for tropical cyclone induced storm surge carried out for the Indian seas were case specific and performed for postlandfall cyclone events. In a recent study the authors developed a comprehensive dataset on pre-computed scenarios of storm-tide and coastal inundation maps for the Odisha State (Sahoo and Bhaskaran, 2017a) utilizing the ADCIRC (Advanced Circulation) model. This dataset is the most comprehensive dataset developed so far for the Indian coast. The pre-computed scenarios comprises information on storm-tide and onshore inundation associated with tropical cyclones based on various combinations of wind speeds, landfall locations, translation speed, angle of approach and tidal levels. The present study utilizes the pre-computed dataset (Sahoo and Bhaskaran, 2017a) to determine the best possible scenario of storm-tide and coastal inundation prior to the landfall of a cyclone using the Artificial Intelligence (AI) technique. In this context, the present study trains a suitable neural network architecture and demonstrates that this technique is highly cost and time effective thereby finding potential practical application in operational forecasting of tropical cyclone induced storm surge and associated flooding. The State of Odisha has a coastline length of about 480 km and a recent study by Sahoo and Bhaskaran (2017a) used the ADCIRC model to generate a comprehensive dataset of storm surge and coastal inundation with a resolution of 200 m for the entire coastline stretch of Odisha State. The logic behind choosing 200 dimensions is to demonstrate the ANN technique that was trained to forecast the storm surge and coastal inundation scenario for the Odisha coastline. In this case the 200 coastal points are separated approximately at every 2 km interval. The AI technique within the broad domain of computational intelligence is being widely recognized today due to its diverse practical application and beneficial value across multiple disciplines. AI techniques have grown attention in the field of ocean and atmospheric studies because of their proven robustness in data handling and accuracy in prediction. Branches of AI such as ANN, GA (Genetic Algorithm), GP (Genetic Programming) are efficiently used to classify and forecast parameters such as ocean waves, Sea Surface Temperature (SST), winds, rainfall, air temperature, tsunami travel time, and many more. Application that used ANN and GP are reported by Deo and Naidu (1999), Charate et al. (2009) for wave forecasting. More and Deo (2003) used this technique for wind forecasting. Makarynskyy (2004)
2. Data and methodology This section briefly describes the dataset used to train the ANN model and also demonstrates the building architecture used to predict storm-tide and associated onshore flooding. Pre-computed scenarios of storm-tide and inundation maps generated for the Odisha coast (Sahoo and Bhaskaran, 2017a) was used to train the ANN model. In another study, the authors (Sahoo and Bhaskaran, 2015) constructed multiple synthetic tracks or the most probable cyclone tracks at a spatial interval of every 40 km along the entire Odisha coast. Synthetic tracks along with relevant meteorological information for various combinations of wind speed, translation speed, angle of approach, and tidal amplitudes were provided as input to the ADCIRC model. This exercise resulted in generating 250 different possible scenarios of storm-tide and coastal inundation (more details are available in Sahoo and Bhaskaran, 2017a). Table 1 provides an overview on the different parameters and their combinations used to construct the comprehensive dataset. Fig. 1 shows the location of Odisha State, and the districts along the 480 km coastline. The Ganjam and Balasore districts are the respective southward/ northward boundaries of Odisha State. Fig. 1 deciphers the maximum tidal amplitude (in m) during the period October 19–25, 1999 associated with the 1999 Odisha Super cyclone from southern to northern boundary of Odisha Coast. The wide range of tide induced water level elevation along the Odisha coast attributed due to the variations in width and slope of the continental shelf off Odisha coast is pivotal modeling studies.
Table 1 List of Parameters used to generate the comprehensive dataset of storm tide and coastal inundation. Striking angle (in degrees)
Wind Speed (in knots)
Landfall Locations separated by 40 km interval alongshore the Odisha coast
Tidal conditions during landfall
40, 80, 90, 120
110, 120, 130, 140, 150
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
Spring Tide, Neap Tide
2
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Fig. 1. Maximum tidal amplitude (in m) during 1999 Odisha Super cyclone and locations along the coastal region of Odisha State.
hinder the learning process, while on the other hand specifying larger number of neurons can also result in over-training. The same holds good with the combination of transfer function in each layer and training function. This needs to be determined initially from trial and error method and that can provide confidence in using the optimum number. For training purpose more than 300 combinations of wind speed during landfall, landfall location (respective latitude and longitude), translation speed, incident angle of cyclone track w.r.t shoreline, and prevalent tidal conditions were used as input variables to ANN model. Similar number of multiple scenarios was also generated for storm tide and cross-shore inundation (generated using above combinations of input parameters) at 200 coastal destinations which being the output from the ANN model. A three-layered feed-forward ANN with two hidden layers and an output layer with multiple dimensions (200) are used in the present study. Tidal information at Paradeep Port is used to train the ANN model, as it is the only source of real time tidal information available for the Odisha coast. Based on trial and error method it is found that 25–30 neurons in each layer is the most ideal and would suffice the optimum network and that resulted in highest predictive accuracy level. Based on an unpublished work, the training functions trainlm (Levenberg-Marquardt) and trainbr (Bayesian Regulation) are found to provide the optimum performance. More mathematical details on the training functions used in the present study can be referred from Hagan and Menhaj (1994) and Foresee and Hagan (1997). Although, the present study signifies that the accuracy level of ANN model using the ‘trainbr’ training function is the highest, the simulation time is also quite high (approximately 3 h). Therefore, the ‘trainlim’ training function is found to be adequate for this study in context to both simulation time and accuracy. The overall network performed better for a combination of tan-sigmoid, log-sigmoid, and pure-linear transfer functions. The landfall location, approach angle, translation speed and wind speed were used as input to the ANN model and the output parameters are the storm tide and coastal inundation. Further, the ANN model was used to estimate the storm tide and coastal
Fig. 2. (a) Schematic representation of a single layer single output ANN model, (b) Network with multiple hidden layers for multi-dimensional output function.
The present study used a multi-layer, multi-output feed-forward ANN model trained with the pre-computed dataset of storm tide and onshore inundation. Fig. 2 provides a schematic illustration of the multi-layer, multi-output ANN model that shows the dependency of ANN on model parameters such as neuron number, transfer function, threshold function (training function) and also the input and output data for training. Specifying less number of neurons by the user can 3
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Fig. 3. Correlation coefficient between ADCIRC simulation and ANN prediction for (a) Storm tide (in m), (b) extent of coastal inundation (in km) during training and testing stages.
inundation for the 1999 Odisha Super Cyclone using the relevant input parameters obtained from archived record of tropical cyclones (presented in Table 1). The ANN predicted output of storm tide and cross shore inundation distance was further validated with the ADCIRC simulations, in situ observations and published records of storm tide and inundation extent that occurred during the Odisha Super cyclone.
parameters given in Table 2 are used as input to the ANN model to forecast the storm tide and coastal inundation. The performance of ANN model in terms of clock time is in the order of seconds and the model accuracy in predicting real-time surge and coastal flooding scenario is discussed in Section 3.1. 3.1. Validation of real-time storm tide and inundation using ANN model
3. Results and discussion
Fig. 4a represents a comparison between ANN predicted and ADCIRC simulated storm-tide during the Odisha Super cyclone surrounding the landfall location. Accuracy level obtained in the prediction of storm-tide is about 92% in terms of regression coefficient compared between ADCIRC simulation and ANN prediction (Fig. 5) for this extreme weather event. The computed maximum cross-shore inundation extent is about 50–60 km. Fig. 6a, b illustrates the comparison between ADCIRC and ANN predicted cross-shore extent of coastal inundation. The study reveals that the performance of ANN model in representing the extent of cross-shore inundation is highly satisfactory. In general, there is a marginal under-prediction in the crossshore inundation extent from ANN model for those locations far away from the cyclone landfall. Prediction accuracy level in terms of regression coefficient is about 94% (Fig. 7). The stormtide during this event predicted using ADCIRC and ANN model is depicted as a spatial plot in Fig. 4b. It clearly represents higher values of storm-tide for Jagatsingpur, Kendrapada, and Bhadrak which were the worst affected districts during this cyclone as Jagatsingpur district lies in the close vicinity of cyclone landfall, whereas Kendrapada and Bhadrak districts lies in the leeward side of the cyclone track. In addition, the presence of low-lying river delta (Sahoo and Bhaskaran, 2017b) has accelerated the storm-tide to create havoc in these districts in terms of severe coastal flooding. The spatial extent of onshore inundation shown in Fig. 6b covers the entire 480 km coastline stretch of Odisha. Inundation extent for most of the locations along southern boundary is less than 200 m. Locations centered along the left side of cyclone track within 150 km from landfall point experienced higher inundation ranging between 5.0 and 27 km. Locations in the right side of cyclone track experienced the highest onshore inundation (27 km–60 km) attributed due to strong winds and the presence of wide continental shelf. It is also evident that coastal inundation extends until the northern boundary of the state (Balasaore) which is around 150 km from the storm center. Depending on the wind speed strength and distribution, the southern belt of coastal Odisha was the least affected during 1999 Odisha Super cyclone. However, the wind speed along with the nature of coastal geomorphology created havoc in the central and the northern coast of Odisha State. Relatively steep continental shelf and topographic slope prevented the occurrence of higher surge height and wider coastal flooding in the Southern part,
Historical archives of tropical cyclone activity for the North Indian Ocean basin indicates that 1999 Odisha Super Cyclone was the worst ever-recorded cyclone that made landfall in Odisha State bordering the East coast of India. The devastation that resulted from this extreme weather event was quite severe in terms of enormous loss to life and property. The maximum wind speed during the time of landfall was about 260 km h−1. The storm surge spanned over 100–150 km stretch of coastline with a maximum surge exceeding 6 m found at the north of the landfall point (Rao et al., 2007). The study by Chittibabu et al. (2004) reported a surge of 7.5 m at Paradeep during this extreme event and that resulted in 9 885 catastrophic deaths and damage worth 2.8 billion USD. Based on post-storm survey and numerical modeling study, Dube et al. (2000) reported surge height exceeding 7 m at Paradeep during the 1999 Odisha Super Cyclone. However, the coastal inundation during this Super cyclone was mentioned as 50–60 km in various post-storm governmental and public reports and 2,00 000 ha of land was reported as infertile by saline intrusion. In the present study, ADCIRC model was used to map the storm tide and coastal inundation that occurred during 1999 Odisha Super Cyclone (Figure-4b, 6b) using the relevant track details from Joint Typhoon Warning Center (JTWC). However, this study lacks a detailed validation of the modeling exercise due to the unavailability of in situ measurements such as buoy deployment over this Indian coast. On the other hand, the skill level of ADCIRC model is very well proven based on simulations carried out for recent very severe cyclones, Phailin in 2013 by Murty et al. (2014). Also, the ADCIRC model showed a high level of confidence with the in situ observations for storm tide and coastal inundation at Paradeep during the 1999 Odisha cyclone (Sahoo and Bhaskaran, 2017a). Hence, the present study uses the ADCIRC simulations as benchmark to validate the ANN results. The ANN model was trained using the scenarios of pre-computed storm-tide and coastal inundation using the state-of-art ADCIRC model generated by Sahoo and Bhaskaran (2017a). Using the combination of training functions and transfer functions that are discussed in the methodology section, an accuracy level of 99% and 92% (Fig. 3) was obtained during the training and testing phase of the ANN model for storm tide and inundation extent respectively. The relevant cyclone 4
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Fig. 4. Comparison between ADCIRC simulated and ANN predicted Storm Tide (in m) surrounding the landfall location associated with 1999 Odisha Super cyclone (a) Axial representation (b) Spatial representation.
Fig. 5. Correlation coefficient for Storm tide (in m) between ADCIRC simulation and ANN prediction. 5
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Fig. 6. Comparison between ADCIRC simulated and ANN predicted Cross-shore Inundation Extent (in km) along the Odisha coast (a) Axial representation (b) Spatial representation. Table 2 Relevant parameters pertaining to 1999 Odisha Super Cyclone. Cyclone
Odisha Super Cyclone (October 1999) −1
Maximum wind speed (in m s ) during landfall Angle of approach (in degrees) Landfall location (Longitude, Latitude in decimal degrees) Translational speed (km h−1) Maximum water level (in m) recorded by tide gauge at Paradeep location Maximum Storm Surge (in m) Maximum Inundation along-shore direction (in km) Maximum Inundation cross-shore direction (in km)
however, the presence of major estuarine lows, shallow and wide continental shelf accelerated the surge to pile up and wide spread coastal flooding in the central and northern parts of Odisha coast. The generalized behavior of storm tide and coastal inundation extent along the diverse topography of Odisha coast can be referred from Sahoo and Bhaskaran (2017a). In case of both storm-tide and coastal inundation (Figs. 4b and 6b), ANN is found to perform marginal under-prediction. However, the overall analysis signifies that the ANN model reproduces the storm-tide
72 105 86.215° E, 20.16° N 14 1.65 6.0 100–150 50–60
and spatial extent of onshore inundation fairly well proving its applicability for real-time operations. Based on the skill level from ANN model it could be ascertained that this technique finds potential importance for real-time prediction of storm-tide and onshore inundation. It is also noted that the ANN model could preserve the observed trend for different locations in Odisha coast. It is therefore recommended that ANN model can be very useful in terms of rapid computation, preserving prediction accuracy, and thereby finds potential application for emergency preparedness and evacuation planning during extreme 6
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conducted under the CoE in Climate Change at IIT Kharagpur. References Barman, R., Prasad Kumar, B., Pandey, P.C., Dube, S.K., 2006. Tsunami travel time prediction using neural networks. Geophys. Res. Lett. 133, L16612. http://doi:10. 1029/2006GL026688. Bhaskaran, P.K., Nayak, S., SubbaReddy, B., Murty, P.L.N., Sen, D., 2013. Performance and validation of a coupled parallel ADCIRC-SWAN model for THANE cyclone in the Bay of Bengal. Environ. Fluid Mech. http://doi:10.1007/s10652-013-9284-5. Bhaskaran, P.K., Pandey, P.C., 2010. Tsunami early warning system: an Indian Ocean perspective. In: Natural and Anthropogenic Disasters: Vulnerability, Preparedness and Mitigation, pp. 100–125. http://doi:10.1007/978-90-481-2498-5_6. Bhaskaran, P.K., Rajesh Kumar, R., Barman, R., Ravichandran, M., 2010. A new approach for deriving temperature and salinity fields in the Indian Ocean using artificial neural networks. J. Mar. Sci. Technol. http://doi:10.1007/s00773-009-0081-2. Bhaskaran, P.K., Rajesh, K.R., Dube, S.K., Rao, A.D., Murty, T., Gangopadhyay, A., Chaudhuri, A., 2008. Tsunami early warning system - an Indian Ocean perspective. J. Earthquake Tsunami 2, 197–226. Charate, S.B., Deo, M.C., Londhe, S.N., 2009. Genetic programming for real time prediction of offshore wind. Int. J. Ships Offshore Struct TSOS 349431. http://doi:10. 1080/17445300802492638. Chittibabu, P., Dube, S.K., Macnabb, J.B., Murty, T.S., Rao, A.D., Mohanty, U.C., Sinha, P.C., 2004. Mitigation of flooding and cyclone hazard in Orissa, India. Nat. Hazards 31 (2), 455–485. http://doi:10.1023/B:NHAZ.0000023362.26409.22. Deo, M.C., Naidu, C.S., 1999. Real time wave forecasting using neural networks. Ocean Eng. 26, 191–203. http://doi:10.1016/S0029-8018(97)10025-7. Dube, S.K., Chittibabu, P., Rao, A.D., Sinha, P.C., Murty, T.S., 2000. Extreme sea levels associated with severe tropical cyclones hitting Orissa coast of India. Mar. Geodes. 23 (2), 75–90. https://doi.org/10.1080/01490410050030652. Foresee, F.D., Hagan, M.T., 1997. Gauss–Newton approximation to Bayesian regularization. In: Proceedings of the International Joint Conference on Neural Networks. IEEE Press, Piscataway, NJ, pp. 1930–1935. Gayathri, R., Murty, P.L.N., Bhaskaran, P.K., Srinivasa Kumar, T., 2016. A numerical study of hypothetical storm surge and coastal inundation for AILA cyclone in the Bay of Bengal. Environ. Fluid Mech. http://doi:10.1007/s10652-015-9434-z. Hagan, M.T., Menhaj, M., 1994. Training feed forward networks with the Marquardt algorithm. IEEE Trans. Neural Network. 5, 989–993. Kisi, O., Cimen, M., 2012. Precipitation forecasting by using wavelet-support vector machine conjunction model. Eng. Appl. Artif. Intell. 25, 783–792. http://doi:10. 1016/j.engappai.2011.11.003. Lee, T.L., 2005. Neural network prediction of a storm surge. Ocean Eng. 33, 483–494. http://doi:10.1016/j.oceaneng.2005.04.012. Lee, T.L., 2009. Predictions of typhoon storm surge in Taiwan using artificial neural networks. Advances in Engg. Software 40, 1200–1206. http://doi:10.1016/j. advengsoft.2007.06.005. Makarynskyy, O., 2004. Improving wave predictions with artificial neural networks. Ocean Eng. 31, 709–724. http://doi:10.1016/j.oceaneng.2003.05.003. More, A., Deo, M.C., 2003. Forecasting wind with neural networks. Mar. Struct. 16, 35–49. http://doi:10.1016/S0951-8339(02)00053-9. Murty, P.L.N., Bhaskaran, P.K., Gayathri, R., Sahoo, B., Srinivasa Kumar, T., SubbaReddy, B., 2016. Numerical study of coastal hydrodynamics using a coupled model for Hudhud cyclone in the Bay of Bengal. Estuar. Coast Shelf Sci. 183, 13–27. Murty, P.L.N., Sandhya, K.G., Bhaskaran, P.K., Jose, F., Gayathri, R., Balakrishnan Nair, T.M., Srinivasa Kumar, T., Shenoi, S.S.C., 2014. A coupled hydrodynamic modeling system for PHAILIN cyclone in the Bay of Bengal. Coast. Eng. 93, 71–81. Needham, H.F., Keim, B.D., 2012. A storm surge database for the US Gulf Coast. Int. J. Climatol. 32, 2108–2123. Rao, A.D., Dash, S., Babu, S.V., Jain, I., 2007. Numerical modeling of cyclone's impact on the ocean-A case study of the Orissa super cyclone. J. Coast Res. 23 (5), 1245–1250. Rietbroek, R., Brunnabend, S.E., Kusche, J., Schroter, J., Dahle, C., 2016. Revisiting the contemporary sea-level budget on global and regional scales. Proc. Natl. Acad. Sci. Unit. States Am. 113, 1504–1509. Sahoo, B., Bhaskaran, P.K., 2015. Assessment on historical cyclone tracks in the Bay of Bengal, east coast of India. Int. J. Climatol. http://doi:10.1002/joc.4331. Sahoo, B., Bhaskaran, P.K., 2017a. A comprehensive database for storm surge induced inundation and their climatic projection for the East coast of India. Int. J. Climatol. http://doi:10.1002/joc.5184. Sahoo, B., Bhaskaran, P.K., 2017b. Multi-hazard risk assessment of coastal vulnerability from tropical cyclones - a GIS based approach for the Odisha coast. J. Environ. Manag. 206, 1166–1178. https://doi.org/10.1016/j.jenvman.2017.10.075. Sztobryn, M., 2003. Forecast of storm surge by means of artificial neural network. J. Sea Res. 317–322. http://doi:10.1016/S1385-1101(03)00024-8. Unnikrishnan, A.S., Rupa Kumar, K., Fernandes, S.E., Michael, G.S., Patwardhan, S.K., 2006. Sea level changes along the Indian coast: observations and Projections. Curr. Sci. 90, 362–368. You, S.H., Lee, Y.H., Lee, W.J., 2012. Parameter estimations of a storm surge model using a genetic algorithm. Nat. Hazards 60, 1157–1165. http://doi:10.1007/s11069-0119900-y. You, S.H., Seo, J.W., 2009. Storm surge prediction using an artificial neural network model and cluster analysis. Nat. Hazards. http://doi:/10.1007/s11069-009-9396-x.
Fig. 7. Correlation coefficient for Cross-shore inundation (in km) between ADCIRC simulation and ANN prediction.
weather conditions. Overall the ANN model is much faster, robust and cost effective in comparison to conventional models used during realtime operation. 4. Summary and conclusions Accurate prediction of storm surge and associated coastal inundation during tropical cyclone landfall is an area of immense scientific interest having direct socio-economic implications. Though sophisticated numerical models were developed over time for real-time operations, an effective, robust and cost-effective tool is indispensable for timely warnings and planning operations. The present study reports on the application of a soft computing tool using Artificial Neural Network to predict the characteristics of storm-tide and associated inundation extent associated with 1999 Odisha Super cyclone as a case study. The ANN model was trained using a comprehensive dataset generated using ADICRC model simulated under various combinations of cyclone parameters for the entire coastline of Odisha State. The study clearly demonstrates the high predictive skill level of ANN model in comparison to conventional model and in situ data. A high level of correlation (92% for storm-tide and 94% for onshore inundation prediction) has been achieved from ANN model thereby recommending its applicability for real-time operations. Conflict of interest We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. Acknowledgments This study was conducted as a part of the Centre of Excellence (CoE) in Climate Change studies established at IIT Kharagpur funded by DST, Government of India. It forms a part of the project ‘Wind-Waves and Extreme Water Level Climate Projections for East Coast of India’
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