- Email: [email protected]
A case-based reasoning approach for land use change prediction
Expert Systems with Applications 37 (2010) 5745–5750
Contents lists available at ScienceDirect
Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
A case-based reasoning approach for land use change prediction Du Yunyan a,*, Wen Wei b, Cao Feng a, Ji Min b a
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China b Geo-Information Science and Engineering College, Shandong University of Science and Technology, Qingdao 266510, China
a r t i c l e
i n f o
Keywords: Artificial intelligence Case-based reasoning (CBR) Land use change Spatial relationship
a b s t r a c t Although has been widely used to study geographical problems, case-based reasoning (CBR) method is far less than perfect and research is in great need of to improve CBR-based geographic data representation modeling, as well as spatial similarity computation and reasoning algorithm. This paper reports an improved CBR-based method for studying the spatially complex land use change. Based on a brief summary of advantages and challenges of current existing quantitative methods, the paper first proposes to introduce the CBR approach for land use change study. A three-component model (‘‘problem”, ‘‘geographic environment”, and ‘‘outcome”) was proposed to represent the land use change cases among which there are complicated and inherent spatial relationships. This paper then presents an algorithm to retrieve the inherent spatial relationships, which are then introduced into the CBR similarity reasoning algorithm to predict land use change. The method was tested by examining the land use change in Pearl River Mouth area in China and yields a similar prediction accuracy of 80% as that derived by applying the Bayesian networks approach to the exact same data. As a result, the CBR-based method proposed in this study provides an effective and explicit solution to represent and solve the complicated geographic problems. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction A variety of methods, including Markov chains, multivariate statistics, optimization, system dynamics, and CLUE/CA, have been widely used to study land use change in different areas (Huang & Cai, 2005; Zhang & Zhang, 2005). Previous studies indicate that these methods obviously have their own pros and cons when applied to study land use change. For example, spatial-related knowledge cannot be effectively integrated into the Markov chains method, although it is useful in predicting short-term land use change (Guo & Ou, 2004). Variation in one variable may be well explained by the raw data in statistical modeling, particularly the multivariate linear regression. However, a linear regression model created from data of one region cannot be directly used to other areas (Shi, Chen, & Pan, 2000). Results from optimization modeling can provide enough information to support decision-making, while the modeling cannot present the dynamic land use change processes (Chomitz & Gray, 1996; Konagaya et al., 1999). Complicated system functions and their relationships to the structure in land use change can be partially explained by the systems dynamics
* Corresponding author. Tel.: +86 10 64888973; fax: +86 10 64889630. E-mail address: [email protected] (Y. Du). 0957-4174/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2010.02.035
modeling. However, scale effect, which is one of the most important issues in studying land use change, cannot be integrated into the systems dynamics modeling (He, Shi, Li, Pan, & Chen, 2004). CLUE/CA modeling is able to better simulate a variety of simultaneous land use change processes in a complicated dynamic system. Unfortunately, previous land use change research results are desperately required before applying CLUE/CA modeling (Li & Ye, 1999; Li & Yeh, 2000, 2001; Xu, Verburg, & Chen, 2000). As a result, we believe that none of the above-mentioned methods is capable to accurately and quantitatively analyze the land use change processes. New approach probably will provide a better alternative if it can assimilate some of the advantages of current available methods. Case-based reasoning (CBR) is an artificial intelligence technology which relies on knowledge from old cases to explain new situation. The method is efficient in simplifying knowledge retrieved from old cases, improving solution to the new situation, as well as in accumulating knowledge for further reasoning (Du et al., 2002). Methodologically, CBR provides a problem-oriented analysis method to solve geographic problems, particularly when driving mechanisms of some geographic processes are not yet fully understood. Based on reasoning from knowledge retrieved or accumulated from old cases, CBR approach provides further quantitative analysis to interpret geographic phenomena and forecast possible change.
5746
Y. Du et al. / Expert Systems with Applications 37 (2010) 5745–5750
CBR approach has been widely adopted and used to study geographical-related problems, and quite a few results have been published (Du, Su, Chou, Yang, & Zhou, 2005; Holt & Benwell, 1999; Jones & Roydhouse, 1994; Li, Xie, & Liao, 2004; Yeh & Shi, 1999). However, in these studies, CBR approach is either directly used without any improvement or only incorporating simple spatial characteristics of geographic phenomena. So far, CBR method has not been used to study complicated geographic phenomena, particularly those with significant territorial and zonal differentiation. We believed that further researches are necessary to improve CBR approach, particularly in its capability of geographic information representation and similarity reasoning. This paper proposes an enhanced CBR approach by adding one new component into the representation model and a new reasoning algorithm to retrieve spatial relationships among cases, and a new reason algorithm to predict future land use change. The improved CBR method was used to study land use change in Zhuhai areas, Guangdong Province of China. Performance evaluation results suggest that the method is efficient and capable in presenting and solving spatially complicated geographic problems.
2. CBR-based approach for land use change prediction 2.1. Case representation model Structure of cases is the essence of CBR approach. In previous studies, dual-mode (‘‘problem” and ‘‘outcome”) was used to describe cases (Du et al., 2002). The ‘‘problem” component only includes simple prior spatial information. Environmental factors affecting geographic phenomena, as well as their own spatial distribution pattern, usually are not fully considered and not incorporated into the ‘‘problem” component. Spatial information is also missing in the ‘‘outcome” component. As a result, this dual-mode representation model significantly restricts the further application of CBR approach in solving complicated geographic problems. Spatial differentiation is obviously one of the most important factors that must be considered when trying to solve geographic problems. A three-component representation model is proposed in this study by adding a new component, ‘‘geographic environment”, into the dual-mode model. By incorporating this new component, effect of geographic environment on the problems is considered. Furthermore, spatial information is also introduced into the ‘‘outcome” component. As a result, there are three major components, ‘‘problem”, ‘‘geographic environment”, and ‘‘outcome”, in the cases. Advantage of this representation model is obvious as not only all characteristics of geographic phenomena but also the indirect spatial relationship between phenomena and environment is all considered to solve the problems. Topology, orientation, and distance relationships between different cases are considered in this new representation model. In this paper, ‘‘problem” is defined as ‘‘to predict land use change during a certain period of time in the study area”. Quantitative indexes, such as area, perimeter, and fractal dimension of land parcels, were used to describe the ‘‘problem” component in the cases. ‘‘Geographic environment” includes any geographic factors which may affect possible land use change. Multiple quantitative indexes were calculated and used to define the spatial relationships between land parcels and the geographic environment. For instance, proximity of a land parcel to the nearest road or river, as well as adjacency relationship to other parcels, were considered and introduced into the ‘‘geographic environment” component. The final component of the cases, ‘‘outcome”, refers to the result of land use change during a certain period of time, for instance, from agricultural to built-up land during the past 5 years.
As a result, land use change cases can be defined by the following equation:
Casei ¼ fSi ; SA1i ; SA2i ; . . . ; SAji ; SR1i ; SR2i ; . . . ; SRli ; Landy1i ! Landy2i g i ¼ 1; 2; . . . ; K;
j ¼ 1; 2; . . . ; M;
l ¼ 1; 2; . . . ; N
m Si ¼ fðx1i ; y1i Þ; ðx2i ; y2i Þ; . . . ; ðxm i ; yi Þg
ð1Þ where i is the case number; Si is the shape and size of case i, represented by coordinate collection of land use parcel boundary; SA1i ; SA2i ; . . . ; SAji , are the attributes (totally M) of case i; SR1i ; SR2i ; . . . ; SRli are quantitative indexes (totally N) of spatial relationships between case i and geographical environment factors; Landy1i and Landy2i are the ‘‘outcome” of the case, i.e., land use change during a certain period of time. 2.2. Spatial relationship retrieving algorithm Previous studies indicate that possible land use change is more or less affected or controlled by geographic environment and surrounding land use types. For instance, the parcels next to the city center or main transportation routes are more likely to be converted into urban land. By contrast, chance is slim for those parcels located in the environment- and ecology-sensitive areas to be converted into urban land. These spatial relationships can be retrieved and incorporated into the ‘‘geographic environment” component as quantitative indexes. Key decision-making spatial relationship, once retrieved, will improve CBR performance and therefore expand its application in studying geographic phenomena. Algorithm used to retrieve inherent spatial relationship was developed based on rough set theoretical approach, a method used to extract information on the basis of learning, reasoning, and inferring knowledge from incomplete or uncertain data (Wang, 2001). No prior knowledge beyond the available dataset is necessary. By using the rough set-based method, classification or decision-making rules can be easily derived through simplifying available knowledge while maintaining higher classification accuracy (Zhang, Wu, & Liang, 2001). The algorithm designed in this study consists of three major functional components: representation of spatial relationship based on rough set theory, discretization, and decision-making rules retrieval. Fig. 1 shows the flow chart used to create collection of spatial relationship between land use change and environment based on rough set theory. On the basis of expert’s experiences or previous results, certain spatial relationships, for instance, adjacency relationship, proximity to river or highway, were chosen and used to construct the rough set collection. These relationships were then converted into quantitative indexes in GIS. A spatial relationship decision-making table was created, with row showing old land use change cases and column indicating condition and decision attributes, i.e., the above-mentioned quantitative indexes that describe the spatial relationship between land use change and geographic environment. Values in the last column in the table are the decision-making attribute, which is also the case ‘‘outcome”. Continuous variables in the spatial relationship decision-making table were then converted into discrete ones using different methods according to the condition attribute values. Finally, spatial relationships were then simplified, and key spatial relationships were retrieved and used as the decision-making rules for further analysis. 2.3. Cases similarity computing and reasoning Nearest neighborhood method was widely used in previous CBR-based research to compute the similarity between cases based on the assumptions that two cases have similar and completely
5747
Y. Du et al. / Expert Systems with Applications 37 (2010) 5745–5750
Fig. 1. Land use change spatial relationships based on rough set theory.
independent attributes. As spatial relationship among cases, as well as between cases and environment, were all considered in the representation model used in this study, nearest neighborhood method was not used to calculate the similarity between cases. Instead, similarity was calculated by using the following equation:
SimilarityCaseði;jÞ ¼ w1 SrðCaseði;jÞÞ þ w2 SaðCaseði;jÞÞ þ w3 SsðCaseði;jÞÞ
ð2Þ
where w1 ; w2 , and w3 are weights assigned to similarity coefficients, and w1 þ w2 þ w3 ¼ 1. SrðCaseði;jÞÞ ; SaðCaseði;jÞÞ , and SsðCaseði;jÞÞ are the similarity coefficients between case i and j in spatial relationship, attributes, and shape, respectively. As to land use change study, usually case ‘‘outcome” is not directly related to land parcel shape, which therefore were not considered in this paper. SaðCaseði;jÞÞ was calculated by using traditional Euclidean distance method. SrðCaseði;jÞÞ was calculated using Eq. (3).
SrðCaseði;jÞÞ ¼
n X k¼1
ðwk SrkðCaseði;jÞÞ Þ
n X
wk ¼ 1
ð3Þ
k¼1
where wk is the weight assigned to the kth spatial relationship (for instance, topology relationship); n is the total number of spatial relationships considered in the representation model. As mentioned above, topology relationship, distance relationship, relationship among cases, and connection between cases and geographic environment were all considered in the case representation model in this study. Different methods were used to calculate similarity coefficient of adjacency topology and continuous spatial relationships (Du, Qin, & Wang, 2005; Guo, Du, & Liu, 2005).
Among these different spatial relationships, adjacency topology relationship plays a more important role in calculating case similarity. In this study, topology relationship was used to describe the possible influence of surrounding land use types on land use change of certain land parcel. An adjacency index was then created to describe the topology relationship, i.e., how well the parcel is connected to adjacent plots. This index is calculated by using Eq. (4)
NiCaseðDÞ ¼ C iCaseðDÞ =C CaseðDÞ
ð4Þ
where NiCaseðDÞ is the adjacency index between case D and its adjacent parcels with land use type i; C iCaseðDÞ is the length of sharing arc between case D and its adjacent parcels with land use type i; C CaseðiÞ is the polygon parameter of case D. Similarity coefficients of the spatial relationship among land use change case D and its adjacent land use types were all calculated using Eq. (4). The maximum adjacency index was accepted as the similarity coefficient for case D. The similarity coefficient was then converted from numeric value into string. As a result, the spatial adjacency relationship coefficient can be calculated by comparing the similarity between two strings. Vector space algorithm (Niu, 2006) was used to calculate the spatial adjacency coefficient by using Eq. (5)
Pn i¼1 W Di W Q i ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi SimðD; QÞ ¼ r P Pn n 2 2 i¼1 W Di i¼1 W Q i
i ¼ 1; 2; . . . ; n
ð5Þ
5748
Y. Du et al. / Expert Systems with Applications 37 (2010) 5745–5750
where n is the total number of land use types; based on the adjacency between cases and surrounding land use types, case feature vector was constructed as CðN 1 ; N2 ; . . . ; N i; N n Þ. W Di is the adjacency index between case D and surrounding land use type i, calculated by Eq. (6).
W Di ¼ NiCaseðDÞ
i ¼ 1; 2; . . . ; n
ð6Þ
W Q i is the adjacency index between case Q and surrounding land use type i, calculated by Eq. (7)
W Q i ¼ NiCaseðQÞ
i ¼ 1; 2; . . . ; n
ð7Þ
where N iCaseðDÞ and N iCaseðQ Þ are the adjacency indexes calculated for case D and case Q, respectively, in Eq. (4). Case reasoning was then performed once similarity coefficients were calculated. Old cases with similarity coefficients greater than an arbitrarily set threshold were first selected, and ‘‘outcome” of these old cases was then analyzed. Land use type corresponding to the ‘‘outcome” with maximum probability was finally accepted as the ‘‘outcome” of current case. 3. Case study 3.1. Study area Land use change between 1995 and 2000 in Zhuhai area (Fig. 2) was studied to test the validity of the CBR-based method proposed in this paper. Data used in this case study were derived from visual interpretation of Landsat TM images acquired in 1995 and 2000. Fig. 2 shows there are agricultural, forest, water, and urban lands in the study area. Majority of the study area is agricultural land. Major rivers, reservoirs, highways, and light-duty roads are also plotted in Fig. 2, showing the spatial relationship between land use types and related geographic environment. Statistical analysis result indicates that between 1995 and 2000, the study area was losing plain nonirrigated land, paddy rice field, rural settlement, and other built-up lands, while gaining paddy rice field, urban land, reservoir, and reclaimed land. 3.2. CBR-based quantitative prediction for land use change 3.2.1. Structure of the cases Three components were constructed for the cases. The first component, ‘‘problem”, is to predict land use change in Zhuhai area from 1995 to 2000. This change was quantitatively described by the variation in perimeter and area of land parcel polygon. ‘‘Geographic environment”, the second component in the case, refers to the distribution of geographic features, including major rivers, reservoirs, city, highways, and light-duty roads. Previous study indicates that land use change is affected by a series of factors, including distance, surrounding land use types, and natural attributes (Li & Ye, 1999; Li & Yeh, 2000). Results indicate that proximity to the city center (Li & Ye, 1997), river (Li & Ye, 2001), reservoir (Li & Ye, 2001), and road (Li & Ye, 1997; Wang, Liu, & Zhou, 2008) significantly affects the potential land use change. As a result, we used two topology indexes and six variables to describe the ‘‘geographic environment” component, including major surrounding land use types in 1995 ðN 1 Þ and in 2000 ðN 2 Þ, distance to the nearest town ðD1 Þ, to the nearest built-up land ðD2 Þ, to the nearest river ðD3 Þ, to the nearest reservoir ðD4 Þ, to the nearest highway ðD5 Þ, and to the nearest light-duty road ðD6 Þ. Land use change result is the ‘‘outcome” of the cases, i.e., predicting major land use types in 2000 based on land use types in 1995. As a result, case can be represented by Eq. (8).
Casei ¼ fID; P i ; Ai ; N1i ; N2i ; D1i ; D2i ; D3i ; D4i ; D5i ; D6i ; Landy1995 ; Landy2000 g i ¼ 1; 2; . . . ; k
ð8Þ
3.2.2. Spatial relationship retrieval and cases library creation Eight spatial indexes in Eq. (8) for each land parcel in Fig. 2 were calculated. Distance indexes ðD1 ; D2 ; . . . ; D6 Þ to the major geographic features were calculated by executing a VBA-based algorithm in ArcMap. The two adjacency indexes ðN 1i ; N 2i Þ were derived by examining topology relationship in GIS database. In Eq. (4), adjacency indexes for all land use types were calculated. However, in order to simplify calculation, only the major three land use types with larger adjacency indexes were introduced into the case library and then used to calculate similarity coefficients. Three land use types are required for each case, otherwise character * was used to ensure that the total number of surrounding land use types is three. Land use change case library was then constructed for the 397 land parcels in the study area (Table 1). Thirty of these parcels were randomly selected to test the prediction accuracy. 3.2.3. Similarity coefficients calculation and reasoning Similar old cases were retrieved using similarity-calculating algorithm. Topological adjacency relationship was first calculated by using Eq. (4) and then similarity coefficients were calculated using Eq. (5). Weights were assigned to similarity coefficients based on various impacts of different indexes on the ‘‘outcome” based on previous research results. 3.2.4. Result explanation Test was performed on 30 randomly selected cases, and result indicates that the prediction accuracy is 80%. No similar cases were found for case 3, 6, 16, 20, and 22 in the library with a threshold set at 70%. 3.3. Comparison to results derived from bayesian networks In order to evaluate the prediction accuracy derived from CBRbased method, we also use Bayesian network method to study land use change in our study area. There are four major procedures in Bayesian network. 3.3.1. Constructing learning and test sets Data sets were randomly selected and assigned to learning set (LS) and test set (TS), with LS and TS, respectively, corresponding to the case library and test library in the CBR method. 3.3.2. Discretization Prior partitioning is necessary before further computation in Bayesian networks. Data were converted into discrete ones by using minimum entropy method. 3.3.3. Bayesian networks learning Order of variables is one of the most important issues. Better variable order will enable Bayesian networks to clearly express knowledge from the data set, reduce the complexity in probability calculation, and simplify network learning. Based on expert’s experiences, this study used the following variable order: Landy2000 ; Landy1995 ; N 1 ,1 N2 ; D1 ; D5 ; D6 , P, A. Discretized variables were indicated by adding subscript _d. In model with fewer variables and plain relationship, intuitive concept of cause and effect can be used to construct Bayesian networks. However, in a model with a large number of variables and 1 In order to simplify discretization, only the major adjacency relationship, i.e., the first letter in columns N 1 and N 2 in Table 1, is considered in this research.
5749
Y. Du et al. / Expert Systems with Applications 37 (2010) 5745–5750
Fig. 2. Land use classification in the study area.
Table 1 The case library of land use change in Zhuhai area from 1995 to 2000. ID 1 2 3 4 5 6 7 8 396 397
P 1071 747 2275 5148 25,628 2150 2380 1458 ... 25,628 2150
A 32,739 18,567 60,565 272,201 8E+06 140,977 186,535 52,556 ... 8E+06 140,977
N1 a oja oja oja nda cjf cjf ald odb ... cjf cjf
N2 a b
jd jdb jdb med c c
jkd jdb ... c c
D1
D2
D3
D4
D5
D6
Landy1995
Landy2000
9747 9483 8943 255.4 9029 13,616 10,788 8404 ... 9029 13,616
567 582 363 204 1256 6006 793 1523 ... 1256 6006
2357 2165 1982 1929 1709 5917 43.3 2772 ... 1709 5917
1556 1759 1317 682 3069 9088 948 709 ... 3069 9088
0 0 0 1236 9288 13,892 7681 0 ... 9288 13,892
0 0 207 3822 2714 6592 5674 1718 ... 2714 6592
d d d g i i j j ... I i
m m m c b
k p m ... b
k
a
sea-water. a: paddy rice field; b: hilly nonirrigated land; c: plain nonirrigated land; d: dense forest; e: sparse forest; f: other forest; g: intermediate grassland; h: sparse grassland; i: river; j: pond and reservoir; k: tidal flat; l: mud flat; m: urban; n: rural settlement sites; o: other built-up land; p: dense grassland. c Adjacent land use types are not considered if they are sea-water. b
complicated relationships, cumulative learning is necessary before creating Bayesian networks. Knowledge can be retrieved through exhaustive learning from huge data set in the database. Those networks best fitting the data and prior knowledge were adopted. Fig. 3 shows the Bayesian networks created on the basis of knowledge learned from LS and using variable order of Landy2000 ; Landy1995 ; N 1 ; N 2 ; D1 ; D5 ; D6 ; P; A. Networks were further improved (Fig. 4) based on different impact of relationship be-
tween urban and built-up land, as well as the inherent relationship between land use types and its size.
3.3.4. Test result Finally, the TS data were imported into the improved Bayesian networks, and prediction accuracy was calculated. The result indicates that 25 of the 30 randomly selected cases were correctly
5750
Y. Du et al. / Expert Systems with Applications 37 (2010) 5745–5750
in studying land use change and other natural resource environmental issues. Acknowledgment This study was fully supported by the National 863 High Technology Programs of China (Project No. 2007AA12Z222) and was granted from the State Key Laboratory of Resource and Environment Information System (Project No. 088RA400SA). The authors would like to sincerely thank the Data Center for Resources and Environmental Sciences Chinese Academy of Sciences (RESDC) for providing the land use data of this study.
Fig. 3. Automatically built Bayesian networks.
Fig. 4. Improved Bayesian networks.
predicted, with an accuracy of 83.33 ± 13.34% at a confidence level of 95%. As the results indicate, both CBR and Bayesian networks approaches yielded similar prediction accuracy. However, advantages in CBR approach are obvious, particularly in dealing with complicated geographic phenomena. When using CBR method, it is not necessary to define those complicated conversion regulations. Instead, the method predicts land use change simply based on knowledge retrieved from old cases, hence significantly improving the efficiency in building the case library, as well as case querying in the library. By contrast, Bayesian networks require extensive computation and more unrealistic assumptions, i.e., complete data set, no preferred selection, and non-continuous variables. 4. Conclusions A new ‘‘geographic environment” component was introduced into case representation model in the CBR approach, which was then used to study the land use change in Zhuhai area. Experiment result indicates method proposed in this paper is simple, flexible, and practical in studying land use change with satisfactory prediction accuracy when compared to the Bayesian networks method. Case library built by using CBR method can be dynamically updated. Self-training is also possible in the CBR method as more cases were accumulated, therefore providing an effective method to study dynamic processes in natural environment. Results also indicate that this method excels the Bayesian networks approach in less extensive computation and not so restricted assumptions. We believed that this method can be quickly adapted and used
References Chomitz, K. M., & Gray, D. A. (1996). Roads, land use, and deforestation: A spatial model of land use in Belize. The World Bank Economic Review, 10(3). Du, Sh. H., Qin, Q. M., & Wang, Q. (2005). Reasoning topological relation from various directional relation in GIS. Computer-Aided Design and Graphics Journal, 17(9), 291–301. Du, Y. Y., Su, F. Zh., Chou, T. Y., Yang, X. M., & Zhou, C. H. (2005). Research of casebased reasoning on marine information space vortex characteristics similar. Journal of Tropical Ocean, 24(3), 1–9. Du, Y. Y., Zhou, Ch. H., Shao, Q. Q., Su, F. Z., Shi, Z. Z., & Ye, S. R. (2002). Theoretic and Application Research of Geo-case Based Reasoning. Geography Journal, 2(57), 151–158. Guo, Q. Sh., Du, X. Ch., & Liu, H. (2005). Quantitative description and abstract research of the spatial topological relation. Acta Geodaetica et Cartographica Sinica, 34(2), 123–128. Guo, Y. Y., & Ou, M. H. (2004). The application of Markov model in land use structure prediction. Land Science of Guangdong, 3, 37–41. He, Ch. Y., Shi, P. J., Li, J. G., Pan, Y. Z., & Chen, J. (2004). The future land use change scenarios simulated in northern China. Acta Geographica Sinica, 59(4), 599–607. Holt, A., & Benwell, G. L. (1999). Applying case-based reasoning techniques in GIS. International Journal of Geographical Information Science, 13(1), 9–25. Huang, Q. H., & Cai, Y. L. (2005). Review on several domestic land use change models. China Land Science, 9(5), 25–30. Jones, E. K., & Roydhouse, A. (1994). Intelligent retrieval of historical meteorological data? Artificial Intelligence Appl., 8(3), 43–54. Konagaya, K., Moritam H., Otsubo, K. (1999). Chinese land use predicted by the GTR2 Model. Discussion paper in the 1999 Open Meeting of the Human Dimensions of Global Environmental Change Research Community, Tokyo. Li, X., Xie, J. A., & Liao, Q. F. (2004). The use of case-based reasoning (CBR) method of radar images for land use classification. Journal of Remote Sensing, 8(3), 246–252. Li, X., & Ye, J.-A. (1997). The use of remote sensing monitoring and analysis urban expansion process of the Pearl River Delta – A case study of Dongguan City. Geographical Research, 16(4), 56–61. Li, X., & Ye, J. A. (1999). Binding unit automatically model the evolution of CA and sustainable urban development patterns simulation. Acta Geographica Sinica, 54(4), 92–101. Li, X., & Ye, J.-A. (2001). Application of principal component analysis and cellular automata at spatial decision and urban simulation. Chinese Science, 31(8), 683–690. Li, X., & Yeh, A. G. O. (2000). Modelling sustainable urban development by the integration of constrained cellular automata and GIS. International Journal of Geographical Information Science, 14(2), 131–152. Li, X., & Yeh, A. G. O. (2001). Zoning for agricultural land protection by the integration of remote sensing, GIS and cellular automata. Photogram Metric Engineering & Remote Sensing, 67(4), 471–477. Niu, L. (2006). Vector space model based on the improvement of the text classification algorithm. Intelligence Magazine, 6, 63–65. Shi, P. J., Chen, J., & Pan, Y. J. (2000). The land-use change mechanism analysis of Shenzhen City. Geography Journal, 55(2), 52–62. Wang, G. Zh. (2001). Rough Set Theory and knowledge acquisition. Xi’an: Xi’an Jiao Tong University (pp. 1–25). Wang, W. Q., Liu, B. X., & Zhou, X. W. (2008). Study of road traffic and land use in Chengdu. Journal of Xihua University (Philosophy and Social Sciences Edition), 27(5), 109–110. Xu, B., Verburg, P., & Chen, Q. (2000). Spatial modeling of land use and its effects in China. Progress in Geography, 19(2), 710–721. Yeh, A. G. O., & Shi, X. (1999). Applying case-based reasoning to urban planning: A new planning support system tool. Environment and Planning B, 26(1), 101–115. Zhang, W. X., Wu, W. Zh., & Liang, J. Y. (2001). Rough set theory and methods. Beijing: Science Press (pp. 1–39). Zhang, H., & Zhang, B. (2005). Review on land use and land cover change models. Natural Resources Journal, 20(3), 422–431.
Contents lists available at ScienceDirect
Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa
A case-based reasoning approach for land use change prediction Du Yunyan a,*, Wen Wei b, Cao Feng a, Ji Min b a
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China b Geo-Information Science and Engineering College, Shandong University of Science and Technology, Qingdao 266510, China
a r t i c l e
i n f o
Keywords: Artificial intelligence Case-based reasoning (CBR) Land use change Spatial relationship
a b s t r a c t Although has been widely used to study geographical problems, case-based reasoning (CBR) method is far less than perfect and research is in great need of to improve CBR-based geographic data representation modeling, as well as spatial similarity computation and reasoning algorithm. This paper reports an improved CBR-based method for studying the spatially complex land use change. Based on a brief summary of advantages and challenges of current existing quantitative methods, the paper first proposes to introduce the CBR approach for land use change study. A three-component model (‘‘problem”, ‘‘geographic environment”, and ‘‘outcome”) was proposed to represent the land use change cases among which there are complicated and inherent spatial relationships. This paper then presents an algorithm to retrieve the inherent spatial relationships, which are then introduced into the CBR similarity reasoning algorithm to predict land use change. The method was tested by examining the land use change in Pearl River Mouth area in China and yields a similar prediction accuracy of 80% as that derived by applying the Bayesian networks approach to the exact same data. As a result, the CBR-based method proposed in this study provides an effective and explicit solution to represent and solve the complicated geographic problems. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction A variety of methods, including Markov chains, multivariate statistics, optimization, system dynamics, and CLUE/CA, have been widely used to study land use change in different areas (Huang & Cai, 2005; Zhang & Zhang, 2005). Previous studies indicate that these methods obviously have their own pros and cons when applied to study land use change. For example, spatial-related knowledge cannot be effectively integrated into the Markov chains method, although it is useful in predicting short-term land use change (Guo & Ou, 2004). Variation in one variable may be well explained by the raw data in statistical modeling, particularly the multivariate linear regression. However, a linear regression model created from data of one region cannot be directly used to other areas (Shi, Chen, & Pan, 2000). Results from optimization modeling can provide enough information to support decision-making, while the modeling cannot present the dynamic land use change processes (Chomitz & Gray, 1996; Konagaya et al., 1999). Complicated system functions and their relationships to the structure in land use change can be partially explained by the systems dynamics
* Corresponding author. Tel.: +86 10 64888973; fax: +86 10 64889630. E-mail address: [email protected] (Y. Du). 0957-4174/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2010.02.035
modeling. However, scale effect, which is one of the most important issues in studying land use change, cannot be integrated into the systems dynamics modeling (He, Shi, Li, Pan, & Chen, 2004). CLUE/CA modeling is able to better simulate a variety of simultaneous land use change processes in a complicated dynamic system. Unfortunately, previous land use change research results are desperately required before applying CLUE/CA modeling (Li & Ye, 1999; Li & Yeh, 2000, 2001; Xu, Verburg, & Chen, 2000). As a result, we believe that none of the above-mentioned methods is capable to accurately and quantitatively analyze the land use change processes. New approach probably will provide a better alternative if it can assimilate some of the advantages of current available methods. Case-based reasoning (CBR) is an artificial intelligence technology which relies on knowledge from old cases to explain new situation. The method is efficient in simplifying knowledge retrieved from old cases, improving solution to the new situation, as well as in accumulating knowledge for further reasoning (Du et al., 2002). Methodologically, CBR provides a problem-oriented analysis method to solve geographic problems, particularly when driving mechanisms of some geographic processes are not yet fully understood. Based on reasoning from knowledge retrieved or accumulated from old cases, CBR approach provides further quantitative analysis to interpret geographic phenomena and forecast possible change.
5746
Y. Du et al. / Expert Systems with Applications 37 (2010) 5745–5750
CBR approach has been widely adopted and used to study geographical-related problems, and quite a few results have been published (Du, Su, Chou, Yang, & Zhou, 2005; Holt & Benwell, 1999; Jones & Roydhouse, 1994; Li, Xie, & Liao, 2004; Yeh & Shi, 1999). However, in these studies, CBR approach is either directly used without any improvement or only incorporating simple spatial characteristics of geographic phenomena. So far, CBR method has not been used to study complicated geographic phenomena, particularly those with significant territorial and zonal differentiation. We believed that further researches are necessary to improve CBR approach, particularly in its capability of geographic information representation and similarity reasoning. This paper proposes an enhanced CBR approach by adding one new component into the representation model and a new reasoning algorithm to retrieve spatial relationships among cases, and a new reason algorithm to predict future land use change. The improved CBR method was used to study land use change in Zhuhai areas, Guangdong Province of China. Performance evaluation results suggest that the method is efficient and capable in presenting and solving spatially complicated geographic problems.
2. CBR-based approach for land use change prediction 2.1. Case representation model Structure of cases is the essence of CBR approach. In previous studies, dual-mode (‘‘problem” and ‘‘outcome”) was used to describe cases (Du et al., 2002). The ‘‘problem” component only includes simple prior spatial information. Environmental factors affecting geographic phenomena, as well as their own spatial distribution pattern, usually are not fully considered and not incorporated into the ‘‘problem” component. Spatial information is also missing in the ‘‘outcome” component. As a result, this dual-mode representation model significantly restricts the further application of CBR approach in solving complicated geographic problems. Spatial differentiation is obviously one of the most important factors that must be considered when trying to solve geographic problems. A three-component representation model is proposed in this study by adding a new component, ‘‘geographic environment”, into the dual-mode model. By incorporating this new component, effect of geographic environment on the problems is considered. Furthermore, spatial information is also introduced into the ‘‘outcome” component. As a result, there are three major components, ‘‘problem”, ‘‘geographic environment”, and ‘‘outcome”, in the cases. Advantage of this representation model is obvious as not only all characteristics of geographic phenomena but also the indirect spatial relationship between phenomena and environment is all considered to solve the problems. Topology, orientation, and distance relationships between different cases are considered in this new representation model. In this paper, ‘‘problem” is defined as ‘‘to predict land use change during a certain period of time in the study area”. Quantitative indexes, such as area, perimeter, and fractal dimension of land parcels, were used to describe the ‘‘problem” component in the cases. ‘‘Geographic environment” includes any geographic factors which may affect possible land use change. Multiple quantitative indexes were calculated and used to define the spatial relationships between land parcels and the geographic environment. For instance, proximity of a land parcel to the nearest road or river, as well as adjacency relationship to other parcels, were considered and introduced into the ‘‘geographic environment” component. The final component of the cases, ‘‘outcome”, refers to the result of land use change during a certain period of time, for instance, from agricultural to built-up land during the past 5 years.
As a result, land use change cases can be defined by the following equation:
Casei ¼ fSi ; SA1i ; SA2i ; . . . ; SAji ; SR1i ; SR2i ; . . . ; SRli ; Landy1i ! Landy2i g i ¼ 1; 2; . . . ; K;
j ¼ 1; 2; . . . ; M;
l ¼ 1; 2; . . . ; N
m Si ¼ fðx1i ; y1i Þ; ðx2i ; y2i Þ; . . . ; ðxm i ; yi Þg
ð1Þ where i is the case number; Si is the shape and size of case i, represented by coordinate collection of land use parcel boundary; SA1i ; SA2i ; . . . ; SAji , are the attributes (totally M) of case i; SR1i ; SR2i ; . . . ; SRli are quantitative indexes (totally N) of spatial relationships between case i and geographical environment factors; Landy1i and Landy2i are the ‘‘outcome” of the case, i.e., land use change during a certain period of time. 2.2. Spatial relationship retrieving algorithm Previous studies indicate that possible land use change is more or less affected or controlled by geographic environment and surrounding land use types. For instance, the parcels next to the city center or main transportation routes are more likely to be converted into urban land. By contrast, chance is slim for those parcels located in the environment- and ecology-sensitive areas to be converted into urban land. These spatial relationships can be retrieved and incorporated into the ‘‘geographic environment” component as quantitative indexes. Key decision-making spatial relationship, once retrieved, will improve CBR performance and therefore expand its application in studying geographic phenomena. Algorithm used to retrieve inherent spatial relationship was developed based on rough set theoretical approach, a method used to extract information on the basis of learning, reasoning, and inferring knowledge from incomplete or uncertain data (Wang, 2001). No prior knowledge beyond the available dataset is necessary. By using the rough set-based method, classification or decision-making rules can be easily derived through simplifying available knowledge while maintaining higher classification accuracy (Zhang, Wu, & Liang, 2001). The algorithm designed in this study consists of three major functional components: representation of spatial relationship based on rough set theory, discretization, and decision-making rules retrieval. Fig. 1 shows the flow chart used to create collection of spatial relationship between land use change and environment based on rough set theory. On the basis of expert’s experiences or previous results, certain spatial relationships, for instance, adjacency relationship, proximity to river or highway, were chosen and used to construct the rough set collection. These relationships were then converted into quantitative indexes in GIS. A spatial relationship decision-making table was created, with row showing old land use change cases and column indicating condition and decision attributes, i.e., the above-mentioned quantitative indexes that describe the spatial relationship between land use change and geographic environment. Values in the last column in the table are the decision-making attribute, which is also the case ‘‘outcome”. Continuous variables in the spatial relationship decision-making table were then converted into discrete ones using different methods according to the condition attribute values. Finally, spatial relationships were then simplified, and key spatial relationships were retrieved and used as the decision-making rules for further analysis. 2.3. Cases similarity computing and reasoning Nearest neighborhood method was widely used in previous CBR-based research to compute the similarity between cases based on the assumptions that two cases have similar and completely
5747
Y. Du et al. / Expert Systems with Applications 37 (2010) 5745–5750
Fig. 1. Land use change spatial relationships based on rough set theory.
independent attributes. As spatial relationship among cases, as well as between cases and environment, were all considered in the representation model used in this study, nearest neighborhood method was not used to calculate the similarity between cases. Instead, similarity was calculated by using the following equation:
SimilarityCaseði;jÞ ¼ w1 SrðCaseði;jÞÞ þ w2 SaðCaseði;jÞÞ þ w3 SsðCaseði;jÞÞ
ð2Þ
where w1 ; w2 , and w3 are weights assigned to similarity coefficients, and w1 þ w2 þ w3 ¼ 1. SrðCaseði;jÞÞ ; SaðCaseði;jÞÞ , and SsðCaseði;jÞÞ are the similarity coefficients between case i and j in spatial relationship, attributes, and shape, respectively. As to land use change study, usually case ‘‘outcome” is not directly related to land parcel shape, which therefore were not considered in this paper. SaðCaseði;jÞÞ was calculated by using traditional Euclidean distance method. SrðCaseði;jÞÞ was calculated using Eq. (3).
SrðCaseði;jÞÞ ¼
n X k¼1
ðwk SrkðCaseði;jÞÞ Þ
n X
wk ¼ 1
ð3Þ
k¼1
where wk is the weight assigned to the kth spatial relationship (for instance, topology relationship); n is the total number of spatial relationships considered in the representation model. As mentioned above, topology relationship, distance relationship, relationship among cases, and connection between cases and geographic environment were all considered in the case representation model in this study. Different methods were used to calculate similarity coefficient of adjacency topology and continuous spatial relationships (Du, Qin, & Wang, 2005; Guo, Du, & Liu, 2005).
Among these different spatial relationships, adjacency topology relationship plays a more important role in calculating case similarity. In this study, topology relationship was used to describe the possible influence of surrounding land use types on land use change of certain land parcel. An adjacency index was then created to describe the topology relationship, i.e., how well the parcel is connected to adjacent plots. This index is calculated by using Eq. (4)
NiCaseðDÞ ¼ C iCaseðDÞ =C CaseðDÞ
ð4Þ
where NiCaseðDÞ is the adjacency index between case D and its adjacent parcels with land use type i; C iCaseðDÞ is the length of sharing arc between case D and its adjacent parcels with land use type i; C CaseðiÞ is the polygon parameter of case D. Similarity coefficients of the spatial relationship among land use change case D and its adjacent land use types were all calculated using Eq. (4). The maximum adjacency index was accepted as the similarity coefficient for case D. The similarity coefficient was then converted from numeric value into string. As a result, the spatial adjacency relationship coefficient can be calculated by comparing the similarity between two strings. Vector space algorithm (Niu, 2006) was used to calculate the spatial adjacency coefficient by using Eq. (5)
Pn i¼1 W Di W Q i ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi SimðD; QÞ ¼ r P Pn n 2 2 i¼1 W Di i¼1 W Q i
i ¼ 1; 2; . . . ; n
ð5Þ
5748
Y. Du et al. / Expert Systems with Applications 37 (2010) 5745–5750
where n is the total number of land use types; based on the adjacency between cases and surrounding land use types, case feature vector was constructed as CðN 1 ; N2 ; . . . ; N i; N n Þ. W Di is the adjacency index between case D and surrounding land use type i, calculated by Eq. (6).
W Di ¼ NiCaseðDÞ
i ¼ 1; 2; . . . ; n
ð6Þ
W Q i is the adjacency index between case Q and surrounding land use type i, calculated by Eq. (7)
W Q i ¼ NiCaseðQÞ
i ¼ 1; 2; . . . ; n
ð7Þ
where N iCaseðDÞ and N iCaseðQ Þ are the adjacency indexes calculated for case D and case Q, respectively, in Eq. (4). Case reasoning was then performed once similarity coefficients were calculated. Old cases with similarity coefficients greater than an arbitrarily set threshold were first selected, and ‘‘outcome” of these old cases was then analyzed. Land use type corresponding to the ‘‘outcome” with maximum probability was finally accepted as the ‘‘outcome” of current case. 3. Case study 3.1. Study area Land use change between 1995 and 2000 in Zhuhai area (Fig. 2) was studied to test the validity of the CBR-based method proposed in this paper. Data used in this case study were derived from visual interpretation of Landsat TM images acquired in 1995 and 2000. Fig. 2 shows there are agricultural, forest, water, and urban lands in the study area. Majority of the study area is agricultural land. Major rivers, reservoirs, highways, and light-duty roads are also plotted in Fig. 2, showing the spatial relationship between land use types and related geographic environment. Statistical analysis result indicates that between 1995 and 2000, the study area was losing plain nonirrigated land, paddy rice field, rural settlement, and other built-up lands, while gaining paddy rice field, urban land, reservoir, and reclaimed land. 3.2. CBR-based quantitative prediction for land use change 3.2.1. Structure of the cases Three components were constructed for the cases. The first component, ‘‘problem”, is to predict land use change in Zhuhai area from 1995 to 2000. This change was quantitatively described by the variation in perimeter and area of land parcel polygon. ‘‘Geographic environment”, the second component in the case, refers to the distribution of geographic features, including major rivers, reservoirs, city, highways, and light-duty roads. Previous study indicates that land use change is affected by a series of factors, including distance, surrounding land use types, and natural attributes (Li & Ye, 1999; Li & Yeh, 2000). Results indicate that proximity to the city center (Li & Ye, 1997), river (Li & Ye, 2001), reservoir (Li & Ye, 2001), and road (Li & Ye, 1997; Wang, Liu, & Zhou, 2008) significantly affects the potential land use change. As a result, we used two topology indexes and six variables to describe the ‘‘geographic environment” component, including major surrounding land use types in 1995 ðN 1 Þ and in 2000 ðN 2 Þ, distance to the nearest town ðD1 Þ, to the nearest built-up land ðD2 Þ, to the nearest river ðD3 Þ, to the nearest reservoir ðD4 Þ, to the nearest highway ðD5 Þ, and to the nearest light-duty road ðD6 Þ. Land use change result is the ‘‘outcome” of the cases, i.e., predicting major land use types in 2000 based on land use types in 1995. As a result, case can be represented by Eq. (8).
Casei ¼ fID; P i ; Ai ; N1i ; N2i ; D1i ; D2i ; D3i ; D4i ; D5i ; D6i ; Landy1995 ; Landy2000 g i ¼ 1; 2; . . . ; k
ð8Þ
3.2.2. Spatial relationship retrieval and cases library creation Eight spatial indexes in Eq. (8) for each land parcel in Fig. 2 were calculated. Distance indexes ðD1 ; D2 ; . . . ; D6 Þ to the major geographic features were calculated by executing a VBA-based algorithm in ArcMap. The two adjacency indexes ðN 1i ; N 2i Þ were derived by examining topology relationship in GIS database. In Eq. (4), adjacency indexes for all land use types were calculated. However, in order to simplify calculation, only the major three land use types with larger adjacency indexes were introduced into the case library and then used to calculate similarity coefficients. Three land use types are required for each case, otherwise character * was used to ensure that the total number of surrounding land use types is three. Land use change case library was then constructed for the 397 land parcels in the study area (Table 1). Thirty of these parcels were randomly selected to test the prediction accuracy. 3.2.3. Similarity coefficients calculation and reasoning Similar old cases were retrieved using similarity-calculating algorithm. Topological adjacency relationship was first calculated by using Eq. (4) and then similarity coefficients were calculated using Eq. (5). Weights were assigned to similarity coefficients based on various impacts of different indexes on the ‘‘outcome” based on previous research results. 3.2.4. Result explanation Test was performed on 30 randomly selected cases, and result indicates that the prediction accuracy is 80%. No similar cases were found for case 3, 6, 16, 20, and 22 in the library with a threshold set at 70%. 3.3. Comparison to results derived from bayesian networks In order to evaluate the prediction accuracy derived from CBRbased method, we also use Bayesian network method to study land use change in our study area. There are four major procedures in Bayesian network. 3.3.1. Constructing learning and test sets Data sets were randomly selected and assigned to learning set (LS) and test set (TS), with LS and TS, respectively, corresponding to the case library and test library in the CBR method. 3.3.2. Discretization Prior partitioning is necessary before further computation in Bayesian networks. Data were converted into discrete ones by using minimum entropy method. 3.3.3. Bayesian networks learning Order of variables is one of the most important issues. Better variable order will enable Bayesian networks to clearly express knowledge from the data set, reduce the complexity in probability calculation, and simplify network learning. Based on expert’s experiences, this study used the following variable order: Landy2000 ; Landy1995 ; N 1 ,1 N2 ; D1 ; D5 ; D6 , P, A. Discretized variables were indicated by adding subscript _d. In model with fewer variables and plain relationship, intuitive concept of cause and effect can be used to construct Bayesian networks. However, in a model with a large number of variables and 1 In order to simplify discretization, only the major adjacency relationship, i.e., the first letter in columns N 1 and N 2 in Table 1, is considered in this research.
5749
Y. Du et al. / Expert Systems with Applications 37 (2010) 5745–5750
Fig. 2. Land use classification in the study area.
Table 1 The case library of land use change in Zhuhai area from 1995 to 2000. ID 1 2 3 4 5 6 7 8 396 397
P 1071 747 2275 5148 25,628 2150 2380 1458 ... 25,628 2150
A 32,739 18,567 60,565 272,201 8E+06 140,977 186,535 52,556 ... 8E+06 140,977
N1 a oja oja oja nda cjf cjf ald odb ... cjf cjf
N2 a b
jd jdb jdb med c c
jkd jdb ... c c
D1
D2
D3
D4
D5
D6
Landy1995
Landy2000
9747 9483 8943 255.4 9029 13,616 10,788 8404 ... 9029 13,616
567 582 363 204 1256 6006 793 1523 ... 1256 6006
2357 2165 1982 1929 1709 5917 43.3 2772 ... 1709 5917
1556 1759 1317 682 3069 9088 948 709 ... 3069 9088
0 0 0 1236 9288 13,892 7681 0 ... 9288 13,892
0 0 207 3822 2714 6592 5674 1718 ... 2714 6592
d d d g i i j j ... I i
m m m c b
k p m ... b
k
a
sea-water. a: paddy rice field; b: hilly nonirrigated land; c: plain nonirrigated land; d: dense forest; e: sparse forest; f: other forest; g: intermediate grassland; h: sparse grassland; i: river; j: pond and reservoir; k: tidal flat; l: mud flat; m: urban; n: rural settlement sites; o: other built-up land; p: dense grassland. c Adjacent land use types are not considered if they are sea-water. b
complicated relationships, cumulative learning is necessary before creating Bayesian networks. Knowledge can be retrieved through exhaustive learning from huge data set in the database. Those networks best fitting the data and prior knowledge were adopted. Fig. 3 shows the Bayesian networks created on the basis of knowledge learned from LS and using variable order of Landy2000 ; Landy1995 ; N 1 ; N 2 ; D1 ; D5 ; D6 ; P; A. Networks were further improved (Fig. 4) based on different impact of relationship be-
tween urban and built-up land, as well as the inherent relationship between land use types and its size.
3.3.4. Test result Finally, the TS data were imported into the improved Bayesian networks, and prediction accuracy was calculated. The result indicates that 25 of the 30 randomly selected cases were correctly
5750
Y. Du et al. / Expert Systems with Applications 37 (2010) 5745–5750
in studying land use change and other natural resource environmental issues. Acknowledgment This study was fully supported by the National 863 High Technology Programs of China (Project No. 2007AA12Z222) and was granted from the State Key Laboratory of Resource and Environment Information System (Project No. 088RA400SA). The authors would like to sincerely thank the Data Center for Resources and Environmental Sciences Chinese Academy of Sciences (RESDC) for providing the land use data of this study.
Fig. 3. Automatically built Bayesian networks.
Fig. 4. Improved Bayesian networks.
predicted, with an accuracy of 83.33 ± 13.34% at a confidence level of 95%. As the results indicate, both CBR and Bayesian networks approaches yielded similar prediction accuracy. However, advantages in CBR approach are obvious, particularly in dealing with complicated geographic phenomena. When using CBR method, it is not necessary to define those complicated conversion regulations. Instead, the method predicts land use change simply based on knowledge retrieved from old cases, hence significantly improving the efficiency in building the case library, as well as case querying in the library. By contrast, Bayesian networks require extensive computation and more unrealistic assumptions, i.e., complete data set, no preferred selection, and non-continuous variables. 4. Conclusions A new ‘‘geographic environment” component was introduced into case representation model in the CBR approach, which was then used to study the land use change in Zhuhai area. Experiment result indicates method proposed in this paper is simple, flexible, and practical in studying land use change with satisfactory prediction accuracy when compared to the Bayesian networks method. Case library built by using CBR method can be dynamically updated. Self-training is also possible in the CBR method as more cases were accumulated, therefore providing an effective method to study dynamic processes in natural environment. Results also indicate that this method excels the Bayesian networks approach in less extensive computation and not so restricted assumptions. We believed that this method can be quickly adapted and used
References Chomitz, K. M., & Gray, D. A. (1996). Roads, land use, and deforestation: A spatial model of land use in Belize. The World Bank Economic Review, 10(3). Du, Sh. H., Qin, Q. M., & Wang, Q. (2005). Reasoning topological relation from various directional relation in GIS. Computer-Aided Design and Graphics Journal, 17(9), 291–301. Du, Y. Y., Su, F. Zh., Chou, T. Y., Yang, X. M., & Zhou, C. H. (2005). Research of casebased reasoning on marine information space vortex characteristics similar. Journal of Tropical Ocean, 24(3), 1–9. Du, Y. Y., Zhou, Ch. H., Shao, Q. Q., Su, F. Z., Shi, Z. Z., & Ye, S. R. (2002). Theoretic and Application Research of Geo-case Based Reasoning. Geography Journal, 2(57), 151–158. Guo, Q. Sh., Du, X. Ch., & Liu, H. (2005). Quantitative description and abstract research of the spatial topological relation. Acta Geodaetica et Cartographica Sinica, 34(2), 123–128. Guo, Y. Y., & Ou, M. H. (2004). The application of Markov model in land use structure prediction. Land Science of Guangdong, 3, 37–41. He, Ch. Y., Shi, P. J., Li, J. G., Pan, Y. Z., & Chen, J. (2004). The future land use change scenarios simulated in northern China. Acta Geographica Sinica, 59(4), 599–607. Holt, A., & Benwell, G. L. (1999). Applying case-based reasoning techniques in GIS. International Journal of Geographical Information Science, 13(1), 9–25. Huang, Q. H., & Cai, Y. L. (2005). Review on several domestic land use change models. China Land Science, 9(5), 25–30. Jones, E. K., & Roydhouse, A. (1994). Intelligent retrieval of historical meteorological data? Artificial Intelligence Appl., 8(3), 43–54. Konagaya, K., Moritam H., Otsubo, K. (1999). Chinese land use predicted by the GTR2 Model. Discussion paper in the 1999 Open Meeting of the Human Dimensions of Global Environmental Change Research Community, Tokyo. Li, X., Xie, J. A., & Liao, Q. F. (2004). The use of case-based reasoning (CBR) method of radar images for land use classification. Journal of Remote Sensing, 8(3), 246–252. Li, X., & Ye, J.-A. (1997). The use of remote sensing monitoring and analysis urban expansion process of the Pearl River Delta – A case study of Dongguan City. Geographical Research, 16(4), 56–61. Li, X., & Ye, J. A. (1999). Binding unit automatically model the evolution of CA and sustainable urban development patterns simulation. Acta Geographica Sinica, 54(4), 92–101. Li, X., & Ye, J.-A. (2001). Application of principal component analysis and cellular automata at spatial decision and urban simulation. Chinese Science, 31(8), 683–690. Li, X., & Yeh, A. G. O. (2000). Modelling sustainable urban development by the integration of constrained cellular automata and GIS. International Journal of Geographical Information Science, 14(2), 131–152. Li, X., & Yeh, A. G. O. (2001). Zoning for agricultural land protection by the integration of remote sensing, GIS and cellular automata. Photogram Metric Engineering & Remote Sensing, 67(4), 471–477. Niu, L. (2006). Vector space model based on the improvement of the text classification algorithm. Intelligence Magazine, 6, 63–65. Shi, P. J., Chen, J., & Pan, Y. J. (2000). The land-use change mechanism analysis of Shenzhen City. Geography Journal, 55(2), 52–62. Wang, G. Zh. (2001). Rough Set Theory and knowledge acquisition. Xi’an: Xi’an Jiao Tong University (pp. 1–25). Wang, W. Q., Liu, B. X., & Zhou, X. W. (2008). Study of road traffic and land use in Chengdu. Journal of Xihua University (Philosophy and Social Sciences Edition), 27(5), 109–110. Xu, B., Verburg, P., & Chen, Q. (2000). Spatial modeling of land use and its effects in China. Progress in Geography, 19(2), 710–721. Yeh, A. G. O., & Shi, X. (1999). Applying case-based reasoning to urban planning: A new planning support system tool. Environment and Planning B, 26(1), 101–115. Zhang, W. X., Wu, W. Zh., & Liang, J. Y. (2001). Rough set theory and methods. Beijing: Science Press (pp. 1–39). Zhang, H., & Zhang, B. (2005). Review on land use and land cover change models. Natural Resources Journal, 20(3), 422–431.