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Sensors (Basel, Switzerland)|March 4, 2020
Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern IndiaSunil Saha, Jagabandhu Roy, Alireza Arabameri, et al.
Sensors (Basel, Switzerland)|January 16, 2020
Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative StudyAlireza Arabameri, Thomas Blaschke, Biswajeet Pradhan, et al.
The Science of the Total Environment|July 1, 2019
Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibilityAlireza Arabameri, Mojtaba Yamani, Biswajeet Pradhan, et al.
The Science of the Total Environment|October 31, 2020
Prediction of landslide susceptibility in Rudraprayag, India using novel ensemble of conditional probability and boosted regression tree-based on cross-validation methodSunil Saha, Alireza Arabameri, Anik Saha, et al.
The Science of the Total Environment|April 23, 2020
A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibilityAlireza Arabameri, Sunil Saha, Jagabandhu Roy, et al.
The Science of the Total Environment|September 4, 2020
Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, IndiaAman Arora, Alireza Arabameri, Manish Pandey, et al.
The Science of the Total Environment|August 31, 2019
Spatial prediction of flood potential using new ensembles of bivariate statistics and artificial intelligence: A case study at the Putna river catchment of RomaniaRomulus Costache, Dieu Tien Bui
The Science of the Total Environment|January 14, 2020
Identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensemblesRomulus Costache, Dieu Tien Bui
The Science of the Total Environment|May 14, 2020
Predicting the deforestation probability using the binary logistic regression, random forest, ensemble rotational forest, REPTree: A case study at the Gumani River Basin, IndiaSunil Saha, Mantosh Saha, Kaustuv Mukherjee, et al.
Scientific Reports|August 13, 2021
Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithmsSunil Saha, Raju Sarkar, Jagabandhu Roy, et al.
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