H
Hossein Hashemi
Researcher at Lund University
Publications - 62
Citations - 1219
Hossein Hashemi is an academic researcher from Lund University. The author has contributed to research in topics: Groundwater recharge & Groundwater. The author has an hindex of 16, co-authored 50 publications receiving 675 citations. Previous affiliations of Hossein Hashemi include Kansas Department of Agriculture, Division of Water Resources & Stanford University.
Papers
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Iran's Land Suitability for Agriculture
TL;DR: The capacity of Iran’s land for sustainable crop production is evaluated based on the soil properties, topography, and climate conditions to improve sustainability and reduce pressure on water resources, land, and ecosystem in Iran.
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Groundwater potential mapping using a novel data-mining ensemble model
Mojtaba Dolat Kordestani,Seyed Amir Naghibi,Hossein Hashemi,Kourosh Ahmadi,Bahareh Kalantar,Biswajeet Pradhan,Biswajeet Pradhan +6 more
TL;DR: In this paper, a novel statistical approach combined with a data-mining ensemble model, through implementing evidential belief function and boosted regression tree (EBF-BRT) algorithms for groundwater potential mapping of the Lordegan aquifer in central Iran, was introduced.
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Intercomparison of trend analysis of Multisatellite Monthly Precipitation Products and Gauge Measurements for River Basins of India
TL;DR: In this article, the authors compared the precipitation trend from the gridded rain gauge data collected by India Meteorological Department (IMD) and Multisatellite High Resolution Precipitation Products (HRPPs) for the river basins of India.
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Application of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors
TL;DR: In this article, the authors developed methods to produce reliable groundwater potential maps (GWPMs) with only digital elevation model (DEM)-derived data as inputs, which can be employed to produce initial information for GW exploitation in areas facing a lack of data.
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Inverse method using boosted regression tree and k-nearest neighbor to quantify effects of point and non-point source nitrate pollution in groundwater
Alireza Motevalli,Alireza Motevalli,Seyed Amir Naghibi,Seyed Amir Naghibi,Hossein Hashemi,Ronny Berndtsson,Biswajeet Pradhan,Biswajeet Pradhan,Vahid Gholami +8 more
TL;DR: In this paper, the authors used boosted regression tree (BRT) and k-nearest neighbor (KNN) data mining techniques to produce a nitrate pollution vulnerability map, which can mitigate effects of subjective judgement on determining importance of different sources and mechanisms for nitrate transport.