Land reserve prediction using different kernel based support vector regression

Abstract: Scientific prediction of cultivated land reserved quantity is important for cultivated land protection. Moreover, it provides guiding significance for relieving the contradictions of land use and ensuring food security. The purpose of this paper is to compare the prediction accuracy of different kernel based support vector regression (SVR), and provide a guideline for cultivated land area prediction. Taking Huizhou city for example, we apply different kernel based SVR to simulate the relationships between cultivated land area and impact factors of land use change. Seven impact factors, including population, socio-economics, industrial structure, living level, agricultural technique and policy, were selected by using the grey correlation method. With the socio-economic statistics of Huizhou city from the statistical yearbook and the policies which have been enacted during 1991 to 2010, corresponding cultivated land areas and influence factors were generated. Using data from 1991 to 2005 as training, SVR based on different kernel functions were employed to build the prediction model for cultivated land areas from 2006―2010. Finally, we apply multiple regressions, BP neural network and SVR based on different kernel functions to predict the cultivated land areas of 2006―2010. According to the predicted values and corresponding actual values, the average relative error and correlation coefficient and the root mean square error were used to validate the performance of different models. The analysis of prediction accuracies showed that the correlation coefficient of multiple regressions stayed at a high level, which reached to 0.970. But the average relative error (13.17%) and the root mean square error (0.173) were biggest. The accuracy of SVR based on polynomial kernel was greatly improved, especially for the average relative error and the root mean square error by comparison with that of multiple regressions. The accuracy of BP neural networks is between that of SVR based on polynomial kernel and SVR based on sigmoid kernel. However, for the BP neural networks based cultivated land area prediction model, the predicted value is difficult to ascertain and is prone to over fitting. The average relative error, the root mean square error and the correlation coefficient of SVR based on RBF kernel is 0.54%, 0.963 and 0.007, respectively. Therefore, it is obvious to find that the model of SVR based on RBF kernel can obtain best accuracy in predicting the cultivated land area, the predicting model of SVR based on sigmoid kernel follows. It is concluded that, in the existing three widely used kernel based SVR models, SVR based on RBF kernel is most suitable to be applied to predict the cultivated land areas, and SVR based on sigmoid kernel follows, while SVR based on polynomial kernels is worst.