Review of machine learning algorithms used in groundwater availability studies in Africa: analysis of geological and climate input variables
Abstract Groundwater is crucial for Africa’s potable water supply, agriculture, and economic development. However, the continent faces challenges with groundwater scarcity due to factors like population growth, climate change, and over-exploitation. Over the past ten years, machine learning has been...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-11-01
|
| Series: | Discover Water |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s43832-024-00109-6 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850127864121262080 |
|---|---|
| author | Haoulata Touré Cyril D. Boateng Solomon S. R. Gidigasu David D. Wemegah Vera Mensah Jeffrey N. A. Aryee Marian A. Osei Jesse Gilbert Samuel K. Afful |
| author_facet | Haoulata Touré Cyril D. Boateng Solomon S. R. Gidigasu David D. Wemegah Vera Mensah Jeffrey N. A. Aryee Marian A. Osei Jesse Gilbert Samuel K. Afful |
| author_sort | Haoulata Touré |
| collection | DOAJ |
| description | Abstract Groundwater is crucial for Africa’s potable water supply, agriculture, and economic development. However, the continent faces challenges with groundwater scarcity due to factors like population growth, climate change, and over-exploitation. Over the past ten years, machine learning has been increasingly and successfully used in groundwater availability studies across the world. This review paper explores the application of machine learning techniques in groundwater availability studies including groundwater level prediction and groundwater potential mapping studies by focusing on some of the studies conducted in Africa. The methodology involved downloading relevant papers, identifying and categorizing the machine learning algorithms employed, and quantifying their use. Geological and climatic variables were also identified, analyzed, and categorized to measure their usage frequency. The different algorithms and input variables extracted from each paper are graphically represented in this document highlighting the most employed ones. The findings suggest that more research needs to be conducted on the use of machine learning algorithms on this topic in Africa. In the reviewed papers Fuzzy-based algorithms are commonly used. The groundwater level prediction studies primarily focus on input variables related to hydrology/hydrogeology, while for potential mapping, geological aspects are the most investigated variables. In terms of climate, precipitation receives the most attention in the reviewed studies. The study highlights the potential of machine learning in improving water resource management and decision-making in the region. |
| format | Article |
| id | doaj-art-0bd05e5b8d9e4470811cfa96d681b3fb |
| institution | OA Journals |
| issn | 2730-647X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Water |
| spelling | doaj-art-0bd05e5b8d9e4470811cfa96d681b3fb2025-08-20T02:33:32ZengSpringerDiscover Water2730-647X2024-11-014112010.1007/s43832-024-00109-6Review of machine learning algorithms used in groundwater availability studies in Africa: analysis of geological and climate input variablesHaoulata Touré0Cyril D. Boateng1Solomon S. R. Gidigasu2David D. Wemegah3Vera Mensah4Jeffrey N. A. Aryee5Marian A. Osei6Jesse Gilbert7Samuel K. Afful8Department of Geological Engineering, Kwame Nkrumah University of Science and TechnologyDepartment of Physics, College of Science, Kwame Nkrumah University of Science and TechnologyDepartment of Geological Engineering, Kwame Nkrumah University of Science and TechnologyDepartment of Physics, College of Science, Kwame Nkrumah University of Science and TechnologyDepartment of Geological Engineering, Kwame Nkrumah University of Science and TechnologyDepartment of Meteorology and Climate Science, Kwame Nkrumah University of Science and TechnologyDepartment of Meteorology and Climate Science, Kwame Nkrumah University of Science and TechnologyDepartment of Meteorology and Climate Science, Kwame Nkrumah University of Science and TechnologyDepartment of Computer Science, Kwame Nkrumah University of Science and TechnologyAbstract Groundwater is crucial for Africa’s potable water supply, agriculture, and economic development. However, the continent faces challenges with groundwater scarcity due to factors like population growth, climate change, and over-exploitation. Over the past ten years, machine learning has been increasingly and successfully used in groundwater availability studies across the world. This review paper explores the application of machine learning techniques in groundwater availability studies including groundwater level prediction and groundwater potential mapping studies by focusing on some of the studies conducted in Africa. The methodology involved downloading relevant papers, identifying and categorizing the machine learning algorithms employed, and quantifying their use. Geological and climatic variables were also identified, analyzed, and categorized to measure their usage frequency. The different algorithms and input variables extracted from each paper are graphically represented in this document highlighting the most employed ones. The findings suggest that more research needs to be conducted on the use of machine learning algorithms on this topic in Africa. In the reviewed papers Fuzzy-based algorithms are commonly used. The groundwater level prediction studies primarily focus on input variables related to hydrology/hydrogeology, while for potential mapping, geological aspects are the most investigated variables. In terms of climate, precipitation receives the most attention in the reviewed studies. The study highlights the potential of machine learning in improving water resource management and decision-making in the region.https://doi.org/10.1007/s43832-024-00109-6Groundwater level predictionGroundwater potential mappingMachine learningAfrica |
| spellingShingle | Haoulata Touré Cyril D. Boateng Solomon S. R. Gidigasu David D. Wemegah Vera Mensah Jeffrey N. A. Aryee Marian A. Osei Jesse Gilbert Samuel K. Afful Review of machine learning algorithms used in groundwater availability studies in Africa: analysis of geological and climate input variables Discover Water Groundwater level prediction Groundwater potential mapping Machine learning Africa |
| title | Review of machine learning algorithms used in groundwater availability studies in Africa: analysis of geological and climate input variables |
| title_full | Review of machine learning algorithms used in groundwater availability studies in Africa: analysis of geological and climate input variables |
| title_fullStr | Review of machine learning algorithms used in groundwater availability studies in Africa: analysis of geological and climate input variables |
| title_full_unstemmed | Review of machine learning algorithms used in groundwater availability studies in Africa: analysis of geological and climate input variables |
| title_short | Review of machine learning algorithms used in groundwater availability studies in Africa: analysis of geological and climate input variables |
| title_sort | review of machine learning algorithms used in groundwater availability studies in africa analysis of geological and climate input variables |
| topic | Groundwater level prediction Groundwater potential mapping Machine learning Africa |
| url | https://doi.org/10.1007/s43832-024-00109-6 |
| work_keys_str_mv | AT haoulatatoure reviewofmachinelearningalgorithmsusedingroundwateravailabilitystudiesinafricaanalysisofgeologicalandclimateinputvariables AT cyrildboateng reviewofmachinelearningalgorithmsusedingroundwateravailabilitystudiesinafricaanalysisofgeologicalandclimateinputvariables AT solomonsrgidigasu reviewofmachinelearningalgorithmsusedingroundwateravailabilitystudiesinafricaanalysisofgeologicalandclimateinputvariables AT daviddwemegah reviewofmachinelearningalgorithmsusedingroundwateravailabilitystudiesinafricaanalysisofgeologicalandclimateinputvariables AT veramensah reviewofmachinelearningalgorithmsusedingroundwateravailabilitystudiesinafricaanalysisofgeologicalandclimateinputvariables AT jeffreynaaryee reviewofmachinelearningalgorithmsusedingroundwateravailabilitystudiesinafricaanalysisofgeologicalandclimateinputvariables AT marianaosei reviewofmachinelearningalgorithmsusedingroundwateravailabilitystudiesinafricaanalysisofgeologicalandclimateinputvariables AT jessegilbert reviewofmachinelearningalgorithmsusedingroundwateravailabilitystudiesinafricaanalysisofgeologicalandclimateinputvariables AT samuelkafful reviewofmachinelearningalgorithmsusedingroundwateravailabilitystudiesinafricaanalysisofgeologicalandclimateinputvariables |