Ensemble hybrid machine learning methods for gully erosion susceptibility mapping: K-fold cross validation approach

作     者：Jagabandhu Roy Sunil Saha 

作者机构：Department of GeographyUniversity of Gour BangaMalda732103West BengalIndia 

出 版 物：《Artificial Intelligence in Geosciences》 (地学人工智能（英文）)

年 卷 期：2022年第3卷第1期

页      面：28-45页

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

主　　题：K-fold cross-validation Gully erosion susceptibility Radial basis function neural network Hybrid ensemble algorithms R-Index 

摘      要：Gully erosion is one of the important problems creating barrier to agricultural *** present research used the radial basis function neural network(RBFnn)and its ensemble with random sub-space(RSS)and rotation forest(RTF)ensemble Meta classifiers for the spatial mapping of gully erosion susceptibility(GES)in Hinglo river basin.120 gullies were marked and grouped into four-fold.A total of 23 factors including topographical,hydrological,lithological,and soil physio-chemical properties were effectively *** maps were built by RBFnn,RSS-RBFnn,and RTF-RBFnn *** very high susceptibility zone of RBFnn,RTF-RBFnn and RSS-RBFnn models covered 6.75%,6.72%and 6.57%in Fold-1,6.21%,6.10%and 6.09%in Fold-2,6.26%,6.13%and 6.05%in Fold-3 and 7%,6.975%and 6.42%in Fold-4 of the *** operating characteristics(ROC)curve and statistical techniques such as mean-absolute-error(MAE),root-mean-absolute-error(RMSE)and relative gully density area(R-index)methods were used for evaluating the GES *** results of the ROC,MAE,RMSE and R-index methods showed that the models of susceptibility to gully erosion have excellent predictive *** simulation results based on machine learning are satisfactory and outstanding and could be used to forecast the areas vulnerable to gully erosion.