Unified modeling based on SVM and SVR for prediction of forest area ratio by human population density and relief energy

Deforestation is caused by various factors. In the literature, the impact of human activities as well as geographic circumstances on forests has been extensively discussed. Tanaka and Nishii have studied statistical models for prediction of forest area ratio by covariates: human population density and relief energy [1-3] observed in a grid-cell system. Parametric non-linear regression functions of the covariates were used for predicting forest coverage ratio [1], and cubic spline functions were also used for detection of small fluctuation of regression functions [2]. Furthermore, zero-one inflated distributions were proposed for classification of each site into one of three categories: completely-deforested, fully-forest-covered or partly-deforested areas [3]. These methods took the spatial dependency into the modeling, which is not an easy task.