Unified modeling based on SVM and SVR for prediction of forest area ration by human population density and relief enargy
Unified modeling based on SVM and SVR for prediction of forest area ration by human population density and relief enargy
复制标题
基于SVM和SVR的人口密度和救济能量森林面积比预测统一建模
DOI：
10.1109/igarss.2015.7326332
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
R. Nishii and S. Tanaka
中科院分区：
文献类型：
--
作者：
山田知美;片岡恒史;高辻俊宏;世良耕一郎;中村 剛;野瀬善明;R. Nishii and S. Tanaka
砍伐森林是由各种因素造成的。文献中广泛讨论了人类活动和地理环境对森林的影响。Tanaka和Nishii研究了通过协变量预测森林面积比率的统计模型：在网格单元系统中观察到的人口密度和地形能量[1-3]。用协变量的参数非线性回归函数预测森林覆盖率[1]，用三次样条函数检测回归函数的微小波动[2]。此外，提出了将每个地点分为三类之一的零一膨胀分布：完全砍伐森林，完全森林覆盖或部分砍伐森林地区[3]。这些方法将空间依赖性引入到建模中，这不是一件容易的事情。
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.