Comparison of CBR and SVM Method Used in the Prediction of Land Use Change in Pearl River Delta, China

Abstract

Many methods have been employed to study Land Use Change (LUC) in different areas, including some new algorithms from Artificial Intelligence (AI) field, such as Case-Based Reasoning (CBR), Artificial Neural Network (ANN), Bayesian Network (BN) and Support Vector Machine (SVM). Applications of some new methods have indicated both advantages and limitations. This paper presents a comparison between CBR and SVM methods, both of them are used to predict the LUC in Pearl River Delta, China in this study. The comparison is made in three respects: estimation accuracy, flexibility and efficiency. The experimental results demonstrate that CBR and SVM are both effective approaches to predict the LUC with the overall accuracy of 80% and 84% respectively. According to the statistical results, when considering all the changed land use categories, CBR is more stable than SVM for LUC estimation in this study. In addition, a complementary experiment for CBR approach is carried out to compare the estimation accuracies of these two methods in the absence of character data. The results indicate that SVM performances much better than CBR method without character data. Considering the running time, the choice of CBR or SVM method for LUC estimation should be based on the number of samples and variables.