Name Biswajeet Pradhan Affiliation پژوهشگر خارجی Degree _ Website Email — JournalPaper Presentation Speech WorkShop Book Innovation GrantAttraction Thesis Festival FinishedProject Sales Art StandardCreation TitleJournal 1 Predicting sustainable arsenic mitigation using machine learning techniques ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2 Swarm intelligence optimization of the group method of data handling using the cuckoo search and whale optimization algorithms to model and predict landslides APPLIED SOFT COMPUTING 3 Hybridized neural fuzzy ensembles for dust source modeling and prediction ATMOSPHERIC ENVIRONMENT 4 SWPT: An automated GIS-based tool for prioritization of sub-watersheds based on morphometric and topo-hydrological factors Geoscience Frontiers 5 A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping Water 6 A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran) SENSORS 7 Modeling flood susceptibility using data-driven approaches of naive Bayes tree, alternating decision tree, and random forest methods SCIENCE OF THE TOTAL ENVIRONMENT 8 Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model Remote Sensing 9 Novel Hybrid Integration Approach of Bagging-Based Fisher’s Linear Discriminant Function for Groundwater Potential Analysis Natural Resources Research 10 A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods JOURNAL OF HYDROLOGY 11 Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm Remote Sensing 12 Multi-Criteria Decision Making (MCDM) Model for Seismic Vulnerability Assessment (SVA) of Urban Residential Buildings ISPRS International Journal of Geo-Information 13 Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping SENSORS 14 Groundwater spring potential modelling: Comprising the capability and robustness of three different modeling approaches JOURNAL OF HYDROLOGY 15 Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms SENSORS 16 A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China) Environmental Earth Sciences 17 Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China SCIENCE OF THE TOTAL ENVIRONMENT