Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree
By Wei Chen, Xia Zhao, Himan Shahabi, Ataollah Shirzadi, Khabat Khosravi, Huichan Chai, Shuai Zhang, Lingyu Zhang, Jianquan Ma, Yingtao Chen and 3 more. Geocarto International, 2019
121 citations recorded by the source corpus.
Abstract In this study, we introduced novel hybrid of evidence believe function (EBF) with logistic regression (EBF-LR) and logistic model tree (EBF-LMT) for landslide susceptibility modelling. Fourteen conditioning factors were selected, including slope aspect, elevation, slope angle, profile curvature, plan curvature, topographic wetness index (TWI), stream sediment transport index (STI), stream power index (SPI), distance to rivers, distance to faults, distance to roads, lithology, normalized difference vegetation index (NDVI), and land use. The importance of factors was assessed using correlation attribute evaluation method. Finally, the performance of three models was evaluated using the area under the curve (AUC). The validation process indicated that the EBF-LMT model acquired the highest AUC for the training (84.7%) and validation (76.5%) datasets, followed by EBF-LR and EBF models. Our result also confirmed that combination of a decision tree-logistic regression-based algorithm with a bivariate statistical model lead to enhance the prediction power of individual landslide models.
Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law, Safety, Risk, Reliability and Quality
View Paper: https://www.semanticscholar.org/paper/f60deba3173064c719b42c3feff1cbb0302c1b07
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In Global and Planetary Change
- Ataollah Shirzadi
- Khabat Khosravi
- Himan Shahabi
- Baharin bin Ahmad
- Kamran Chapi
- N. Al‐Ansari
- Marten Geertsema
- John J. Clague
In Management, Monitoring, Policy and Law
- Ataollah Shirzadi
- Baharin bin Ahmad
- Kamran Chapi
- Himan Shahabi
- Marten Geertsema
- Khabat Khosravi
- Jie Dou
- John J. Clague
In Safety, Risk, Reliability and Quality
Citations in the corpus
- Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran (2020, background): “1 and VIF < 10, there is a correlation among the factors and the factor with having such information should be removed from the modeling process [48,75].”
- Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier (2020, methodology): “Although ANFIS is a powerful algorithm and has higher predictive power than both ANN and FL, its membership function fails to adequately determine optimum weights [34,35], hence an optimization algorithm has been applied to calculate optimum values automatically [8,9,36,37].”