- Email: [email protected]
Comments on “Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques” by A. Moghaddamnia, M. Ghafari Gousheh, J. Piri, S. Amin, D. Han [Adv. Water Resour. 32 (2009) 88–97]
Advances in Water Resources 32 (2009) 966
Contents lists available at ScienceDirect
Advances in Water Resources journal homepage: www.elsevier.com/locate/advwatres
Comments on ‘‘Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques” by A. Moghaddamnia, M. Ghafari Gousheh, J. Piri, S. Amin, D. Han [Adv. Water Resour. 32 (2009) 88–97] The discusser wishes to thank the authors for examining the potential of artificial neural networks (ANNs) and adaptive neurofuzzy inference system (ANFIS) techniques in evaporation estimation. The discusser would like to present the following important points of view, which the authors and potential researchers need to consider. 1. The reliability of the Gamma test (GT) was not taken into consideration in the study. The Gamma test results were given for the five different input combinations in Table 2. The performances of the ANN or ANFIS for each input combination could be investigated to examine the reliability of the GT (see [5]). Or the GT technique could be compared to another method e.g., principle component analysis. According to the GT, the best input combination is selected as W, RH, Ed for the ANN and ANFIS. To the discusser, the models with the inputs of W, T, RH, Ed may perform better than the model with three inputs. 2. In the ‘Results and discussion’ section on p. 92, the authors say that: ‘‘It has been found that the relative importance of inputs is W > Ed > RH > T. The significance of the daily mean temperature data was relatively small when compared with other weather variables since the elimination of this input made small variation in the Gamma statistic value.”. However, the similar studies [1,7,9] in the literature showed that the T and/or RH are more effective on evaporation than the W. 3. In the study, the comparison with conventional empirical evaporation models have not been performed properly. The ANN and ANFIS models were calibrated and validated for the selected site; in contrary the empirical models are through for any location. Thus a fair comparison would be completed either by adjusting the empirical models (using a multiplicator for the calibration period) or by transferring the ANN and ANFIS models to a different site. The examples of the comparison of ANN models with the calibrated empirical models can be seen in the related literature [2–4,6]. The study used only one station for the comparison of ANN, ANFIS and empirical models. Hence the good performance of the ANN and ANFIS models may be peculiar to that particular station. 4. The optimum hidden node numbers have not been given for the ANN models in the study. The authors have also not given any information about the epoch numbers used for the training of the ANN and ANFIS models. A high number of iteration may cause an overtraining problem. On the other hand, the ANN is very sensitive to the selected initial weight values and may provide performance which differs significantly under different applications in MATLAB. The discusser wonders how the authors coped with this problem. 0309-1708/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.advwatres.2009.02.005
5. It could be inferred from Table 4 and Figs. 6 and 7 that the testing performances of the ANN and ANFIS models seem to be better than their training performances. On the contrary, it would be expected that the models should have better accuracy in training/calibration period. These results imply that the ANN and ANFIS models could not learn the evaporation process since they were not well trained. 6. The authors gave the scatterplots of the models’ estimates in Figs. 6 and 7 but they did not comment on these graphs in the manuscript. In addition, the ranges of the x- and y-axis in scatterplots given in Figs. 6 and 7 are not appropriate. The same ranges should have been selected to better see the closeness of the estimates to the exact line. 7. The RMSE formula has not been provided in the manuscript. However, the RMSE unit which has been illustrated in Table 4 and Figs. 6 and 7 seems to be incorrect. To the discusser, the unit of the RMSE should be the same with the unit of the evaporation (mm). The data-driven models should be carefully established. Various model structures and parameters should be tried. Otherwise, the model results may be contrary to the physics of the examined phenomenon. References [1] Sudheer KP, Gosain AK, Mohana Rangan D, Saheb SM. Modelling evaporation using an artificial neural network algorithm. Hydrol Process 2002;16:3189–202. [2] Trajkovic S. Temperature-based approaches for estimating reference evapotranspiration. ASCE J Irrigation Drain Eng 2005;131(4):316–23. [3] Kisi O. Evapotranspiration estimation using feed-forward neural networks. Nordic Hydrol 2006;37(3):247–60. [4] Kisi O. Generalized regression neural networks for evapotranspiration modelling. Hydrol Sci J 2006;51(6):1092–105. [5] Kisi O. Constructing neural network sediment estimation models using a datadriven algorithm. Math Comput Simul 2008;79(1):94–103. [6] Kisi O. The potential of different ANN techniques in evapotranspiration modelling. Hydrol Process 2008;22:1449–2460. [7] Kisi. Daily pan evaporation modelling using multi-layer perceptrons and radial basis neural networks. Hydrol Process 2009;23:213–23. [8] Moghaddamnia A, Ghafari Gousheh M, Piri J, Amin S, Han D. Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv Water Res 2009;32:88–97. [9] Keskin ME, Terzi O. Artificial neural network models of daily pan evaporation. ASCE J Hydrol Eng 2006;11:65–70.
Ozgur Kisi Erciyes University, Engineering Faculty, Civil Engineering Department, Hydraulics Division, 38039 Kayseri, Turkey Tel.: +90 352 437 0080; fax: +90 352 437 5784 E-mail address: [email protected]
Contents lists available at ScienceDirect
Advances in Water Resources journal homepage: www.elsevier.com/locate/advwatres
Comments on ‘‘Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques” by A. Moghaddamnia, M. Ghafari Gousheh, J. Piri, S. Amin, D. Han [Adv. Water Resour. 32 (2009) 88–97] The discusser wishes to thank the authors for examining the potential of artificial neural networks (ANNs) and adaptive neurofuzzy inference system (ANFIS) techniques in evaporation estimation. The discusser would like to present the following important points of view, which the authors and potential researchers need to consider. 1. The reliability of the Gamma test (GT) was not taken into consideration in the study. The Gamma test results were given for the five different input combinations in Table 2. The performances of the ANN or ANFIS for each input combination could be investigated to examine the reliability of the GT (see [5]). Or the GT technique could be compared to another method e.g., principle component analysis. According to the GT, the best input combination is selected as W, RH, Ed for the ANN and ANFIS. To the discusser, the models with the inputs of W, T, RH, Ed may perform better than the model with three inputs. 2. In the ‘Results and discussion’ section on p. 92, the authors say that: ‘‘It has been found that the relative importance of inputs is W > Ed > RH > T. The significance of the daily mean temperature data was relatively small when compared with other weather variables since the elimination of this input made small variation in the Gamma statistic value.”. However, the similar studies [1,7,9] in the literature showed that the T and/or RH are more effective on evaporation than the W. 3. In the study, the comparison with conventional empirical evaporation models have not been performed properly. The ANN and ANFIS models were calibrated and validated for the selected site; in contrary the empirical models are through for any location. Thus a fair comparison would be completed either by adjusting the empirical models (using a multiplicator for the calibration period) or by transferring the ANN and ANFIS models to a different site. The examples of the comparison of ANN models with the calibrated empirical models can be seen in the related literature [2–4,6]. The study used only one station for the comparison of ANN, ANFIS and empirical models. Hence the good performance of the ANN and ANFIS models may be peculiar to that particular station. 4. The optimum hidden node numbers have not been given for the ANN models in the study. The authors have also not given any information about the epoch numbers used for the training of the ANN and ANFIS models. A high number of iteration may cause an overtraining problem. On the other hand, the ANN is very sensitive to the selected initial weight values and may provide performance which differs significantly under different applications in MATLAB. The discusser wonders how the authors coped with this problem. 0309-1708/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.advwatres.2009.02.005
5. It could be inferred from Table 4 and Figs. 6 and 7 that the testing performances of the ANN and ANFIS models seem to be better than their training performances. On the contrary, it would be expected that the models should have better accuracy in training/calibration period. These results imply that the ANN and ANFIS models could not learn the evaporation process since they were not well trained. 6. The authors gave the scatterplots of the models’ estimates in Figs. 6 and 7 but they did not comment on these graphs in the manuscript. In addition, the ranges of the x- and y-axis in scatterplots given in Figs. 6 and 7 are not appropriate. The same ranges should have been selected to better see the closeness of the estimates to the exact line. 7. The RMSE formula has not been provided in the manuscript. However, the RMSE unit which has been illustrated in Table 4 and Figs. 6 and 7 seems to be incorrect. To the discusser, the unit of the RMSE should be the same with the unit of the evaporation (mm). The data-driven models should be carefully established. Various model structures and parameters should be tried. Otherwise, the model results may be contrary to the physics of the examined phenomenon. References [1] Sudheer KP, Gosain AK, Mohana Rangan D, Saheb SM. Modelling evaporation using an artificial neural network algorithm. Hydrol Process 2002;16:3189–202. [2] Trajkovic S. Temperature-based approaches for estimating reference evapotranspiration. ASCE J Irrigation Drain Eng 2005;131(4):316–23. [3] Kisi O. Evapotranspiration estimation using feed-forward neural networks. Nordic Hydrol 2006;37(3):247–60. [4] Kisi O. Generalized regression neural networks for evapotranspiration modelling. Hydrol Sci J 2006;51(6):1092–105. [5] Kisi O. Constructing neural network sediment estimation models using a datadriven algorithm. Math Comput Simul 2008;79(1):94–103. [6] Kisi O. The potential of different ANN techniques in evapotranspiration modelling. Hydrol Process 2008;22:1449–2460. [7] Kisi. Daily pan evaporation modelling using multi-layer perceptrons and radial basis neural networks. Hydrol Process 2009;23:213–23. [8] Moghaddamnia A, Ghafari Gousheh M, Piri J, Amin S, Han D. Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv Water Res 2009;32:88–97. [9] Keskin ME, Terzi O. Artificial neural network models of daily pan evaporation. ASCE J Hydrol Eng 2006;11:65–70.
Ozgur Kisi Erciyes University, Engineering Faculty, Civil Engineering Department, Hydraulics Division, 38039 Kayseri, Turkey Tel.: +90 352 437 0080; fax: +90 352 437 5784 E-mail address: [email protected]