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

Naghibi, Seyed Amir LU ; Hashemi, Hossein LU orcid ; Berndtsson, Ronny LU orcid and Lee, Saro (2020) In Journal of Hydrology 589.
Abstract

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... (More)

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.

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author
; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Data scarcity, Extreme gradient boosting, GIS, Groundwater potential, Parallel random forest
in
Journal of Hydrology
volume
589
article number
125197
publisher
Elsevier
external identifiers
  • scopus:85086823476
ISSN
0022-1694
DOI
10.1016/j.jhydrol.2020.125197
language
English
LU publication?
yes
id
511100dc-abcf-46d9-9a87-5487e7b285fd
date added to LUP
2020-07-07 09:32:25
date last changed
2023-10-08 07:47:18
@article{511100dc-abcf-46d9-9a87-5487e7b285fd,
  abstract     = {{<p>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.</p>}},
  author       = {{Naghibi, Seyed Amir and Hashemi, Hossein and Berndtsson, Ronny and Lee, Saro}},
  issn         = {{0022-1694}},
  keywords     = {{Data scarcity; Extreme gradient boosting; GIS; Groundwater potential; Parallel random forest}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Journal of Hydrology}},
  title        = {{Application of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors}},
  url          = {{http://dx.doi.org/10.1016/j.jhydrol.2020.125197}},
  doi          = {{10.1016/j.jhydrol.2020.125197}},
  volume       = {{589}},
  year         = {{2020}},
}