Remote sensing assessment of dam impact on arid basins in Southern Saudi Arabia: A machine learning and space-for-time approach
Study region: This study focuses on four basins in southern Saudi Arabia: Hali, Baish, Yiba, and Reem. These regions are characterized by arid conditions and are significantly impacted by dam construction. Study focus: The research investigates the environmental impacts of dam construction using a s...
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Elsevier
2025-04-01
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| Series: | Journal of Hydrology: Regional Studies |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S221458182500045X |
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| author | Raid Almalki Mehdi Khaki Patricia M. Saco Jose F. Rodriguez |
| author_facet | Raid Almalki Mehdi Khaki Patricia M. Saco Jose F. Rodriguez |
| author_sort | Raid Almalki |
| collection | DOAJ |
| description | Study region: This study focuses on four basins in southern Saudi Arabia: Hali, Baish, Yiba, and Reem. These regions are characterized by arid conditions and are significantly impacted by dam construction. Study focus: The research investigates the environmental impacts of dam construction using a space-for-time substitution approach, remote sensing, and machine learning techniques. A key focus is analyzing non-linear environmental impacts, particularly in data-limited, arid regions where traditional methodologies fall short. The study introduces a novel framework that combines space-for-time substitution and Dynamic Time Warping (DTW) to assess temporal and spatiotemporal changes in key environmental factors such as NDVI, soil salinity, groundwater, and runoff. New hydrological insights: The results reveal significant changes post-dam construction. In the Yiba-Hali basins, DTW values increased across several parameters: NDVI (0.08–0.25), soil salinity (0.09–0.25), and runoff (0.45–0.90), indicating reduced similarity between pre- and post-dam conditions. In the Reem-Baish basins, the Baish dam caused notable increases in DTW values for NDVI (0.16–0.31), soil salinity (0.15–0.30), groundwater (0.52–1.19), and runoff (0.53–1.33), with the most significant changes observed in groundwater and runoff. Additionally, regression models showed a decrease in predictive accuracy from 2010 to 2020, as evidenced by lower R² values for NDVI (0.82–0.37), soil salinity (0.77–0.38), groundwater (0.98–0.34), and soil moisture (0.96–0.24). |
| format | Article |
| id | doaj-art-b2df7864296b4036ac2909418530affa |
| institution | Kabale University |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-b2df7864296b4036ac2909418530affa2025-02-05T04:32:07ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-04-0158102221Remote sensing assessment of dam impact on arid basins in Southern Saudi Arabia: A machine learning and space-for-time approachRaid Almalki0Mehdi Khaki1Patricia M. Saco2Jose F. Rodriguez3School of Environmental and Life Science, University of Newcastle, Callaghan, NSW 2308, Australia; Department of Geography, Umm Al-Qura University, Makkah 21955, Saudi Arabia; Corresponding author at: School of Environmental and Life Science, University of Newcastle, Callaghan, NSW 2308, Australia.School of Engineering, University of Newcastle, Callaghan, NSW 2308, AustraliaSchool of Engineering, University of Newcastle, Callaghan, NSW 2308, Australia; School of Civil and Environmental Engineering University of Technology Sydney, Ultimo, NSW 2007, AustraliaSchool of Engineering, University of Newcastle, Callaghan, NSW 2308, AustraliaStudy region: This study focuses on four basins in southern Saudi Arabia: Hali, Baish, Yiba, and Reem. These regions are characterized by arid conditions and are significantly impacted by dam construction. Study focus: The research investigates the environmental impacts of dam construction using a space-for-time substitution approach, remote sensing, and machine learning techniques. A key focus is analyzing non-linear environmental impacts, particularly in data-limited, arid regions where traditional methodologies fall short. The study introduces a novel framework that combines space-for-time substitution and Dynamic Time Warping (DTW) to assess temporal and spatiotemporal changes in key environmental factors such as NDVI, soil salinity, groundwater, and runoff. New hydrological insights: The results reveal significant changes post-dam construction. In the Yiba-Hali basins, DTW values increased across several parameters: NDVI (0.08–0.25), soil salinity (0.09–0.25), and runoff (0.45–0.90), indicating reduced similarity between pre- and post-dam conditions. In the Reem-Baish basins, the Baish dam caused notable increases in DTW values for NDVI (0.16–0.31), soil salinity (0.15–0.30), groundwater (0.52–1.19), and runoff (0.53–1.33), with the most significant changes observed in groundwater and runoff. Additionally, regression models showed a decrease in predictive accuracy from 2010 to 2020, as evidenced by lower R² values for NDVI (0.82–0.37), soil salinity (0.77–0.38), groundwater (0.98–0.34), and soil moisture (0.96–0.24).http://www.sciencedirect.com/science/article/pii/S221458182500045XRemote sensingSpace-for-timeMachine learningDam impactsWaterArid regions |
| spellingShingle | Raid Almalki Mehdi Khaki Patricia M. Saco Jose F. Rodriguez Remote sensing assessment of dam impact on arid basins in Southern Saudi Arabia: A machine learning and space-for-time approach Journal of Hydrology: Regional Studies Remote sensing Space-for-time Machine learning Dam impacts Water Arid regions |
| title | Remote sensing assessment of dam impact on arid basins in Southern Saudi Arabia: A machine learning and space-for-time approach |
| title_full | Remote sensing assessment of dam impact on arid basins in Southern Saudi Arabia: A machine learning and space-for-time approach |
| title_fullStr | Remote sensing assessment of dam impact on arid basins in Southern Saudi Arabia: A machine learning and space-for-time approach |
| title_full_unstemmed | Remote sensing assessment of dam impact on arid basins in Southern Saudi Arabia: A machine learning and space-for-time approach |
| title_short | Remote sensing assessment of dam impact on arid basins in Southern Saudi Arabia: A machine learning and space-for-time approach |
| title_sort | remote sensing assessment of dam impact on arid basins in southern saudi arabia a machine learning and space for time approach |
| topic | Remote sensing Space-for-time Machine learning Dam impacts Water Arid regions |
| url | http://www.sciencedirect.com/science/article/pii/S221458182500045X |
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