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Hossein Hashemi

Senior lecturer


Application of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors


  • Seyed Amir Naghibi
  • Hossein Hashemi
  • Ronny Berndtsson
  • Saro Lee

Summary, in English

Groundwater (GW) resources provide a large share of the world's water demand for various sections such as agriculture, industry, and drinking water. Particularly in the arid and semi-arid regions, with surface water scarcity and high evaporation, GW is a valuable commodity. Yet, GW data are often incomplete or nonexistent. Therefore, it is a challenge to achieve a GW potential assessment. In this study, we developed methods to produce reliable GW potential maps (GWPM) with only digital elevation model (DEM)-derived data as inputs. To achieve this objective, a case study area in Iran was selected and 13 factors were extracted from the DEM. A spring location dataset was obtained from the water sector organizations and, along with the non-spring locations, fed into machine learning algorithms for training and validation. For delineating reliable GW potential, algorithms including random forest (RF) and its developed version, parallel RF (PRF), as well as extreme gradient boosting (XGB) with different boosters were used. The area under the receiver operating characteristics curve indicated that the PRF and XGB with linear booster give similar high accuracy (about 86%) for GWPM. The most important factors for accurate GWPM in the modeling procedure were convergence, topographic wetness index, river density, and altitude. Overall, we conclude that high-accuracy GWPMs can be produced with only DEM-derived factors with acceptable accuracy. The developed methodology can be employed to produce initial information for GW exploitation in areas facing a lack of data.


  • Division of Water Resources Engineering
  • Centre for Advanced Middle Eastern Studies
  • MECW: The Middle East in the Contemporary World

Publishing year





Journal of Hydrology



Document type

Journal article




  • Oceanography, Hydrology, Water Resources


  • Data scarcity
  • Extreme gradient boosting
  • GIS
  • Groundwater potential
  • Parallel random forest




  • ISSN: 0022-1694