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[1]ZHAO Yan-ling,HE Ting-ting,LIU Ya-ping,et al.Prediction of Cultivated Land Change of Anhui Province Based on FSA-LSSVR Model[J].Research of Soil and Water Conservation,2014,21(03):136-140.

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Prediction of Cultivated Land Change of Anhui Province Based on FSA-LSSVR Model

Research of Soil and Water Conservation[ISSN 1005-3409/CN 61-1272/P] Volume: 21 Number of periods: 2014 03 Page number: 136-140 Column: Public date: 2014-06-28

Title:: Prediction of Cultivated Land Change of Anhui Province Based on FSA-LSSVR Model

Author(s):: ZHAO Yan-ling, HE Ting-ting, LIU Ya-ping, SHI Juan-juan, RAN Yan-yan, NI Wei, WU Guo-wei; Institute of Land Reclamation and Ecological Reconstruction, China University of Mining & Technology(Beijing), Beijing 100083, China

Keywords:: land change; driving factors; Fish Swarm Algorithm; least square support sector regression

CLC:: F301.24

DOI:: -

Abstract:: With respect to accordance with the internal law of cultivated land change and the characteristics of external driving factors, the change predictive model of cultivated land was developed by using the method of FSA (Fish Swarm Algorithm) optimize Least Square Support Vector Regression (FSA-LSSVR) model. It could provide reference for cultivated land protection policy. The results indicated that the global search capability of FSA could make the support vector machine algorithm effectively converge to global optimal solution of the parameters γ and σ. Furthermore, the FSA-LSSVR model prediction accuracy index was much higher than the multiple, GM (1,1) and BP neural network model, and it was superior to FSA-SVM, the speed of processing data is obviously better than the SVM. Therefore, it concluded that the FSA-LSSVR model could solve the problem of SVM internal parameters which were difficult to be determined. It was applicable to many factors involved in high-dimensional nonlinear prediction of cultivated land change. Moreover, it was high speed, high precision and worthy of promotion.

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