سامانه مدیریت امور پژوهشی دانشگاه کردستان | بیسواجیت پرادهان

نام و نام خانوادگی	بیسواجیت پرادهان
شغل	پژوهشگر خارجی
تحصیلات	دکترای تخصصی / GIS modelling
وبسایت
پست الکترونیک	—

	عنوان	مجله
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