A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China
作者： Wei Chen , Ataollah Shirzadi , Himan Shahabi , Baharin Bin Ahmad , Shuai Zhang
DOI: 10.1080/19475705.2017.1401560
关键词:
摘要: ABSTRACTThe main objective of this study was to produce landslide susceptibility maps for Langao County, China, using a novel hybrid artificial intelligence method based on rotation forest ensembles (RFEs) and naive Bayes tree (NBT) classifiers labeled the RF-NBT model. The spatial database consisted eighteen conditioning factors that were selected information gain ratio (IGR) method. model evaluated quantitative statistical criteria, including sensitivity, specificity, accuracy, root mean squared error (RMSE), area under receiver operating characteristic curve (AUC). Furthermore, new compared with NBT, functional (FT), logistic (LMT) reduced-error pruning (REPTree) soft computing benchmark models. findings indicated showed an increased prediction accuracy relative NBT both training validation datasets, exhibited greater capability susc...
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