Abstract

The rapid expansion of human settlement, agricultural land and roads because of population growth in several regions of the world has contributed to the depletion of forest land. In this study, novel ensemble intelligent approaches using bagging, dagging and rotation forest (RTF) as meta classifiers of multilayer perceptron (MLP) were used to predict spatial deforestation probability (DP) in Gumani Basin, India. The success rate and correctness of prediction of the ensemble models were compared with MLP. A total of 1000 deforested pixels and 14 deforestation determining factors (DDFs) were used. The ensemble models were trained using 70% of the deforested pixels and validated with the remaining 30%. DDFs were chosen by applying the information gain ratio and Relief-F test methods. Distance to settlement, population growth and distance to roads were the most important factors. The results of DP modelling demonstrated that nearly 16.82%–12.64% of the basin had very high DP. All four models created DP maps with reasonable prediction accuracy and goodness of fit, but the best map was produced by MLP-bagging. The accuracy of the MLP neural net model was increased 2-3% after ensemble with the hybrid meta classifiers (RTF, bagging and dagging). The proposed method could be used for deforestation prediction in other areas having similar geo-environmental conditions. Furthermore, the findings might be used as a basis for future research and could help planners in forest management.

摘要

由于世界一些地区人口的增长，人类住区，农业用地和道路的迅速扩张导致了林地的枯竭。在这项研究中，使用装袋，打gg和轮作林（RTF）作为多层感知器（MLP）的元分类器的新型集成智能方法来预测印度古马尼盆地的空间毁林概率（DP）。将集成模型的预测成功率和正确性与MLP进行了比较。总共使用了1000个森林砍伐像素和14个森林砍伐决定因素（DDF）。使用70％的森林砍伐像素对集成模型进行了训练，并用剩余的30％进行了验证。通过应用信息增益比和Relief-F测试方法选择DDF。到结算的距离，人口增长和道路距离是最重要的因素。DP建模的结果表明，近16.82％–12.64％的盆地具有很高的DP。这四个模型都创建了具有合理的预测精度和拟合优度的DP映射，但是最好的映射是由MLP-bagging生成的。使用混合元分类器（RTF，装袋和拖拽）合在一起后，MLP神经网络模型的准确性提高了2-3％。所提出的方法可用于具有类似地球环境条件的其他地区的森林砍伐预测。此外，这些发现可以用作未来研究的基础，并可以帮助森林管理规划者。这四个模型均以合理的预测精度和拟合优度创建了DP地图，但最佳地图是由MLP-bagging生成的。在使用混合元分类器（RTF，装袋和拖拽）合奏后，MLP神经网络模型的准确性提高了2-3％。所提出的方法可用于具有类似地球环境条件的其他地区的森林砍伐预测。此外，这些发现可以用作未来研究的基础，并可以帮助森林管理规划者。这四个模型均以合理的预测精度和拟合优度创建了DP地图，但最佳地图是由MLP-bagging生成的。在使用混合元分类器（RTF，装袋和拖拽）合奏后，MLP神经网络模型的准确性提高了2-3％。所提出的方法可用于具有类似地球环境条件的其他地区的森林砍伐预测。此外，这些发现可以用作未来研究的基础，并可以帮助森林管理规划者。