Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data
Kamali Maskooni, Ehsan LU
; Naghibi, Seyed Amir
LU
; Hashemi, Hossein
LU
and Berndtsson, Ronny
LU
(2020)
In Remote Sensing
12(17).
- Abstract
- Groundwater (GW) is being uncontrollably exploited in various parts of the world resulting from huge needs for water supply as an outcome of population growth and industrialization. Bearing in mind the importance of GW potential assessment in reaching sustainability, this study seeks to use remote sensing (RS)-derived driving factors as an input of the advanced machine learning algorithms (MLAs), comprising deep boosting and logistic model trees to evaluate their efficiency. To do so, their results are compared with three benchmark MLAs such as boosted regression trees, k-nearest neighbors, and random forest. For this purpose, we firstly assembled different topographical, hydrological, RS-based, and lithological driving factors such as... (More)
- Groundwater (GW) is being uncontrollably exploited in various parts of the world resulting from huge needs for water supply as an outcome of population growth and industrialization. Bearing in mind the importance of GW potential assessment in reaching sustainability, this study seeks to use remote sensing (RS)-derived driving factors as an input of the advanced machine learning algorithms (MLAs), comprising deep boosting and logistic model trees to evaluate their efficiency. To do so, their results are compared with three benchmark MLAs such as boosted regression trees, k-nearest neighbors, and random forest. For this purpose, we firstly assembled different topographical, hydrological, RS-based, and lithological driving factors such as altitude, slope degree, aspect, slope length, plan curvature, profile curvature, relative slope position, distance from rivers, river density, topographic wetness index, land use/land cover (LULC), normalized difference vegetation index (NDVI), distance from lineament, lineament density, and lithology. The GW spring indicator was divided into two classes for training (434 springs) and validation (186 springs) with a proportion of 70:30. The training dataset of the springs accompanied by the driving factors were incorporated into the MLAs and the outputs were validated by different indices such as accuracy, kappa, receiver operating characteristics (ROC) curve, specificity, and sensitivity. Based upon the area under the ROC curve, the logistic model tree (87.813%) generated similar performance to deep boosting (87.807%), followed by boosted regression trees (87.397%), random forest (86.466%), and k-nearest neighbors (76.708%) MLAs. The findings confirm the great performance of the logistic model tree and deep boosting algorithms in modelling GW potential. Thus, their application can be suggested for other areas to obtain an insight about GW-related barriers toward sustainability. Further, the outcome based on the logistic model tree algorithm depicts the high impact of the RS-based factor, such as NDVI with 100 relative influence, as well as high influence of the distance from river, altitude, and RSP variables with 46.07, 43.47, and 37.20 relative influence, respectively, on GW potential. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/5c201896-694d-418e-855b-7b216523a7f3
- author
- Kamali Maskooni, Ehsan
LU
; Naghibi, Seyed Amir
LU
; Hashemi, Hossein
LU
and Berndtsson, Ronny
LU
- organization
- publishing date
- 2020-08-24
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Remote sensing, machine learning, GIS, hydrology, groundwater potential
- in
- Remote Sensing
- volume
- 12
- issue
- 17
- article number
- 2742
- pages
- 24 pages
- publisher
- MDPI AG
- external identifiers
-
- scopus:85091335973
- ISSN
- 2072-4292
- DOI
- 10.3390/rs12172742
- language
- English
- LU publication?
- yes
- id
- 5c201896-694d-418e-855b-7b216523a7f3
- date added to LUP
- 2020-08-25 17:30:04
- date last changed
- 2023-10-08 10:03:44
@article{5c201896-694d-418e-855b-7b216523a7f3,
abstract = {{Groundwater (GW) is being uncontrollably exploited in various parts of the world resulting from huge needs for water supply as an outcome of population growth and industrialization. Bearing in mind the importance of GW potential assessment in reaching sustainability, this study seeks to use remote sensing (RS)-derived driving factors as an input of the advanced machine learning algorithms (MLAs), comprising deep boosting and logistic model trees to evaluate their efficiency. To do so, their results are compared with three benchmark MLAs such as boosted regression trees, k-nearest neighbors, and random forest. For this purpose, we firstly assembled different topographical, hydrological, RS-based, and lithological driving factors such as altitude, slope degree, aspect, slope length, plan curvature, profile curvature, relative slope position, distance from rivers, river density, topographic wetness index, land use/land cover (LULC), normalized difference vegetation index (NDVI), distance from lineament, lineament density, and lithology. The GW spring indicator was divided into two classes for training (434 springs) and validation (186 springs) with a proportion of 70:30. The training dataset of the springs accompanied by the driving factors were incorporated into the MLAs and the outputs were validated by different indices such as accuracy, kappa, receiver operating characteristics (ROC) curve, specificity, and sensitivity. Based upon the area under the ROC curve, the logistic model tree (87.813%) generated similar performance to deep boosting (87.807%), followed by boosted regression trees (87.397%), random forest (86.466%), and k-nearest neighbors (76.708%) MLAs. The findings confirm the great performance of the logistic model tree and deep boosting algorithms in modelling GW potential. Thus, their application can be suggested for other areas to obtain an insight about GW-related barriers toward sustainability. Further, the outcome based on the logistic model tree algorithm depicts the high impact of the RS-based factor, such as NDVI with 100 relative influence, as well as high influence of the distance from river, altitude, and RSP variables with 46.07, 43.47, and 37.20 relative influence, respectively, on GW potential.}},
author = {{Kamali Maskooni, Ehsan and Naghibi, Seyed Amir and Hashemi, Hossein and Berndtsson, Ronny}},
issn = {{2072-4292}},
keywords = {{Remote sensing; machine learning; GIS; hydrology; groundwater potential}},
language = {{eng}},
month = {{08}},
number = {{17}},
publisher = {{MDPI AG}},
series = {{Remote Sensing}},
title = {{Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data}},
url = {{http://dx.doi.org/10.3390/rs12172742}},
doi = {{10.3390/rs12172742}},
volume = {{12}},
year = {{2020}},
}