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Deforestation modeling based on statistical and machine learning approaches

Hold Date
2015-06-23 12:00〜2015-06-23 13:00
Meeting Room (#122), Faculty of Mathematics building, Ito Campus, Kyushu University
Object person
Ryuei Nishii (Institute of Mathematics for Industry Kyushu University)

 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. We have studied statistical models for prediction of forest area ratio by covariates: human population density and relief energy observed in a grid-cell system. Parametric non-linear regression functions of the covariates were used for predicting forest coverage ratio and cubic spline functions were also used for detection of small fluctuation of regression functions. 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. These methods took the spatial dependency into the modeling, which is not an easy task.
 Our aim here is to substitute the previous statistical approach for machine learning approach based on SVM (support vector machine) and SVR (support vector regression). SVM will be used for classification of each site into one of the above-mentioned categories, and SVR for prediction of the forest coverage ratio. The proposed approach implements a neighbors' effect into the modeling easily. By our numerical study, it will be shown that the performance of the machine learning methods is comparable or superior to that of the statistical methods.