Spatial modeling of brine level and salinity in the Qarhan Salt Lake using GIS and automated machine learning algorithms
Study region: Qarhan Salt Lake, the largest salt lake in China, located in the Qaidam Basin. Study focus: Sustainable management of brine resources in Qarhan Salt Lake is crucial due to the impacts of sustained brine pumping, which has altered brine level and salinity distributions. This study devel...
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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/S2214581825000199 |
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| author | Dongmei Yu Zitao Wang Chao Yue Jianping Wang |
| author_facet | Dongmei Yu Zitao Wang Chao Yue Jianping Wang |
| author_sort | Dongmei Yu |
| collection | DOAJ |
| description | Study region: Qarhan Salt Lake, the largest salt lake in China, located in the Qaidam Basin. Study focus: Sustainable management of brine resources in Qarhan Salt Lake is crucial due to the impacts of sustained brine pumping, which has altered brine level and salinity distributions. This study developed an automated machine learning (AutoML) approach to model brine levels and salinity, providing a tool for informed resource management decisions. The Geodetector was employed to quantify the influence of various factors on these parameters. New hydrological insights for the region: An integrated approach using AutoML and GIS significantly improved prediction accuracy for both brine levels and salinity. For brine level prediction, the LightGBM (LGBM) model performed best, achieving an R2 of 0.880 (training) and 0.869 (testing). For salinity, Random Forest (RF) was optimal, with an R2 of 0.895 (training) and 0.881 (testing). Geodetector analysis revealed that distance to pumps (q = 0.544), canal density (q = 0.346), lithology (q = 0.324), and distance to lakes (q = 0.260) are key factors influencing brine levels. For salinity, precipitation (q = 0.350) and distance to lakes (q = 0.097) were found to be the most influential. This study demonstrates AutoML's effectiveness in modeling brine dynamics and offers insights into factors influencing changes, aiding brine extraction optimization and sustainable resource management in fragile salt lake ecosystems. |
| format | Article |
| id | doaj-art-8edf64776c04455f9485f540fcfb2cc1 |
| institution | DOAJ |
| issn | 2214-5818 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Hydrology: Regional Studies |
| spelling | doaj-art-8edf64776c04455f9485f540fcfb2cc12025-08-20T02:57:44ZengElsevierJournal of Hydrology: Regional Studies2214-58182025-04-015810219510.1016/j.ejrh.2025.102195Spatial modeling of brine level and salinity in the Qarhan Salt Lake using GIS and automated machine learning algorithmsDongmei Yu0Zitao Wang1Chao Yue2Jianping Wang3Key Laboratory of Comprehensive and Highly Efficient Utilization of Salt Lake Resources, Qinghai Institute of Salt Lakes, Chinese Academy of Sciences, Xining 810008, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaSchool of Earth and Environment, Anhui University of Science and Technology. Huainan 232001, China; Corresponding author.Key Laboratory of Comprehensive and Highly Efficient Utilization of Salt Lake Resources, Qinghai Institute of Salt Lakes, Chinese Academy of Sciences, Xining 810008, China; University of Chinese Academy of Sciences, Beijing 100049, ChinaKey Laboratory of Comprehensive and Highly Efficient Utilization of Salt Lake Resources, Qinghai Institute of Salt Lakes, Chinese Academy of Sciences, Xining 810008, ChinaStudy region: Qarhan Salt Lake, the largest salt lake in China, located in the Qaidam Basin. Study focus: Sustainable management of brine resources in Qarhan Salt Lake is crucial due to the impacts of sustained brine pumping, which has altered brine level and salinity distributions. This study developed an automated machine learning (AutoML) approach to model brine levels and salinity, providing a tool for informed resource management decisions. The Geodetector was employed to quantify the influence of various factors on these parameters. New hydrological insights for the region: An integrated approach using AutoML and GIS significantly improved prediction accuracy for both brine levels and salinity. For brine level prediction, the LightGBM (LGBM) model performed best, achieving an R2 of 0.880 (training) and 0.869 (testing). For salinity, Random Forest (RF) was optimal, with an R2 of 0.895 (training) and 0.881 (testing). Geodetector analysis revealed that distance to pumps (q = 0.544), canal density (q = 0.346), lithology (q = 0.324), and distance to lakes (q = 0.260) are key factors influencing brine levels. For salinity, precipitation (q = 0.350) and distance to lakes (q = 0.097) were found to be the most influential. This study demonstrates AutoML's effectiveness in modeling brine dynamics and offers insights into factors influencing changes, aiding brine extraction optimization and sustainable resource management in fragile salt lake ecosystems.http://www.sciencedirect.com/science/article/pii/S2214581825000199Spatial modelingBrine levelSalinityQarhan Salt LakeAutomated machine learningGeodetector |
| spellingShingle | Dongmei Yu Zitao Wang Chao Yue Jianping Wang Spatial modeling of brine level and salinity in the Qarhan Salt Lake using GIS and automated machine learning algorithms Journal of Hydrology: Regional Studies Spatial modeling Brine level Salinity Qarhan Salt Lake Automated machine learning Geodetector |
| title | Spatial modeling of brine level and salinity in the Qarhan Salt Lake using GIS and automated machine learning algorithms |
| title_full | Spatial modeling of brine level and salinity in the Qarhan Salt Lake using GIS and automated machine learning algorithms |
| title_fullStr | Spatial modeling of brine level and salinity in the Qarhan Salt Lake using GIS and automated machine learning algorithms |
| title_full_unstemmed | Spatial modeling of brine level and salinity in the Qarhan Salt Lake using GIS and automated machine learning algorithms |
| title_short | Spatial modeling of brine level and salinity in the Qarhan Salt Lake using GIS and automated machine learning algorithms |
| title_sort | spatial modeling of brine level and salinity in the qarhan salt lake using gis and automated machine learning algorithms |
| topic | Spatial modeling Brine level Salinity Qarhan Salt Lake Automated machine learning Geodetector |
| url | http://www.sciencedirect.com/science/article/pii/S2214581825000199 |
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