Critical role of vegetation and human activity indicators in the prediction of shallow groundwater quality distribution in Jianghan Plain with LightGBM algorithm and SHAP analysis
Groundwater serves as an indispensable resource for freshwater, but its quality has experienced a notable decline over recent decades. Spatial prediction of groundwater quality (GWQ) can effectively assist managers in groundwater remediation, management, and risk control. Based on the traditional in...
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| Veröffentlicht in: | Chemosphere (Oxford) Jg. 376; S. 144278 |
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01.05.2025
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| Abstract | Groundwater serves as an indispensable resource for freshwater, but its quality has experienced a notable decline over recent decades. Spatial prediction of groundwater quality (GWQ) can effectively assist managers in groundwater remediation, management, and risk control. Based on the traditional intrinsic groundwater vulnerability (IGV) model (DRASTIC) and three vegetation (V) indicators (NDVI, EVI, and kNDVI) and four human activity (H) indicators (land use, GDP, urbanization index, and nighttime light), we constructed four models for GWQ spatial prediction in the Jianghan Plain (JHP), namely DRASTI, DRASTIH, DRASTIV, and DRASTIVH, excluding the conductivity (C) indicator due to its uniformly low values. LightGBM algorithm, Tree-structured Parzen Estimator (TPE) optimization method, and SHapley Additive exPlanations (SHAP) analysis are used for model setting, calibration, and interpretation, respectively. The results show that nitrogen-related GWQ parameters have higher weights, and the model performs exceptionally well when considering all the indicators (accuracy = 0.840, precision = 0.824, recall = 0.832, F1 score = 0.828, AUROC = 0.914). Notably, the introduced indicators (NDVI, EVI, kNDVI, nighttime light, GDP, and urbanization index) rank as the top six in terms of importance, while traditional DRASTI and land use indicators show lower significance. Based on SHAP analysis, poor GWQ primarily occurs in areas with either extremely high or extremely low GDP and urbanization index values, and human activities are the primary cause of poor GWQ in JHP, potentially involving urbanization, industrial and agricultural activities, as well as fertilizer usage. Finally, the methodological framework proposed in this study is encouraged to be applied to diverse regions, such as plains, karst areas, mountainous regions, and coastal areas, to support effective future groundwater management.
[Display omitted]
•The spatial prediction of groundwater's entropy based WQI was showcased.•Four models were compared based on traditional DRASTIC framework.•LightGBM algorithm and SHAP analysis were employed.•Vegetation and human activity indicators have high importance. |
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| AbstractList | Groundwater serves as an indispensable resource for freshwater, but its quality has experienced a notable decline over recent decades. Spatial prediction of groundwater quality (GWQ) can effectively assist managers in groundwater remediation, management, and risk control. Based on the traditional intrinsic groundwater vulnerability (IGV) model (DRASTIC) and three vegetation (V) indicators (NDVI, EVI, and kNDVI) and four human activity (H) indicators (land use, GDP, urbanization index, and nighttime light), we constructed four models for GWQ spatial prediction in the Jianghan Plain (JHP), namely DRASTI, DRASTIH, DRASTIV, and DRASTIVH, excluding the conductivity (C) indicator due to its uniformly low values. LightGBM algorithm, Tree-structured Parzen Estimator (TPE) optimization method, and SHapley Additive exPlanations (SHAP) analysis are used for model setting, calibration, and interpretation, respectively. The results show that nitrogen-related GWQ parameters have higher weights, and the model performs exceptionally well when considering all the indicators (accuracy = 0.840, precision = 0.824, recall = 0.832, F
score = 0.828, AUROC = 0.914). Notably, the introduced indicators (NDVI, EVI, kNDVI, nighttime light, GDP, and urbanization index) rank as the top six in terms of importance, while traditional DRASTI and land use indicators show lower significance. Based on SHAP analysis, poor GWQ primarily occurs in areas with either extremely high or extremely low GDP and urbanization index values, and human activities are the primary cause of poor GWQ in JHP, potentially involving urbanization, industrial and agricultural activities, as well as fertilizer usage. Finally, the methodological framework proposed in this study is encouraged to be applied to diverse regions, such as plains, karst areas, mountainous regions, and coastal areas, to support effective future groundwater management. Groundwater serves as an indispensable resource for freshwater, but its quality has experienced a notable decline over recent decades. Spatial prediction of groundwater quality (GWQ) can effectively assist managers in groundwater remediation, management, and risk control. Based on the traditional intrinsic groundwater vulnerability (IGV) model (DRASTIC) and three vegetation (V) indicators (NDVI, EVI, and kNDVI) and four human activity (H) indicators (land use, GDP, urbanization index, and nighttime light), we constructed four models for GWQ spatial prediction in the Jianghan Plain (JHP), namely DRASTI, DRASTIH, DRASTIV, and DRASTIVH, excluding the conductivity (C) indicator due to its uniformly low values. LightGBM algorithm, Tree-structured Parzen Estimator (TPE) optimization method, and SHapley Additive exPlanations (SHAP) analysis are used for model setting, calibration, and interpretation, respectively. The results show that nitrogen-related GWQ parameters have higher weights, and the model performs exceptionally well when considering all the indicators (accuracy = 0.840, precision = 0.824, recall = 0.832, F1 score = 0.828, AUROC = 0.914). Notably, the introduced indicators (NDVI, EVI, kNDVI, nighttime light, GDP, and urbanization index) rank as the top six in terms of importance, while traditional DRASTI and land use indicators show lower significance. Based on SHAP analysis, poor GWQ primarily occurs in areas with either extremely high or extremely low GDP and urbanization index values, and human activities are the primary cause of poor GWQ in JHP, potentially involving urbanization, industrial and agricultural activities, as well as fertilizer usage. Finally, the methodological framework proposed in this study is encouraged to be applied to diverse regions, such as plains, karst areas, mountainous regions, and coastal areas, to support effective future groundwater management.Groundwater serves as an indispensable resource for freshwater, but its quality has experienced a notable decline over recent decades. Spatial prediction of groundwater quality (GWQ) can effectively assist managers in groundwater remediation, management, and risk control. Based on the traditional intrinsic groundwater vulnerability (IGV) model (DRASTIC) and three vegetation (V) indicators (NDVI, EVI, and kNDVI) and four human activity (H) indicators (land use, GDP, urbanization index, and nighttime light), we constructed four models for GWQ spatial prediction in the Jianghan Plain (JHP), namely DRASTI, DRASTIH, DRASTIV, and DRASTIVH, excluding the conductivity (C) indicator due to its uniformly low values. LightGBM algorithm, Tree-structured Parzen Estimator (TPE) optimization method, and SHapley Additive exPlanations (SHAP) analysis are used for model setting, calibration, and interpretation, respectively. The results show that nitrogen-related GWQ parameters have higher weights, and the model performs exceptionally well when considering all the indicators (accuracy = 0.840, precision = 0.824, recall = 0.832, F1 score = 0.828, AUROC = 0.914). Notably, the introduced indicators (NDVI, EVI, kNDVI, nighttime light, GDP, and urbanization index) rank as the top six in terms of importance, while traditional DRASTI and land use indicators show lower significance. Based on SHAP analysis, poor GWQ primarily occurs in areas with either extremely high or extremely low GDP and urbanization index values, and human activities are the primary cause of poor GWQ in JHP, potentially involving urbanization, industrial and agricultural activities, as well as fertilizer usage. Finally, the methodological framework proposed in this study is encouraged to be applied to diverse regions, such as plains, karst areas, mountainous regions, and coastal areas, to support effective future groundwater management. Groundwater serves as an indispensable resource for freshwater, but its quality has experienced a notable decline over recent decades. Spatial prediction of groundwater quality (GWQ) can effectively assist managers in groundwater remediation, management, and risk control. Based on the traditional intrinsic groundwater vulnerability (IGV) model (DRASTIC) and three vegetation (V) indicators (NDVI, EVI, and kNDVI) and four human activity (H) indicators (land use, GDP, urbanization index, and nighttime light), we constructed four models for GWQ spatial prediction in the Jianghan Plain (JHP), namely DRASTI, DRASTIH, DRASTIV, and DRASTIVH, excluding the conductivity (C) indicator due to its uniformly low values. LightGBM algorithm, Tree-structured Parzen Estimator (TPE) optimization method, and SHapley Additive exPlanations (SHAP) analysis are used for model setting, calibration, and interpretation, respectively. The results show that nitrogen-related GWQ parameters have higher weights, and the model performs exceptionally well when considering all the indicators (accuracy = 0.840, precision = 0.824, recall = 0.832, F1 score = 0.828, AUROC = 0.914). Notably, the introduced indicators (NDVI, EVI, kNDVI, nighttime light, GDP, and urbanization index) rank as the top six in terms of importance, while traditional DRASTI and land use indicators show lower significance. Based on SHAP analysis, poor GWQ primarily occurs in areas with either extremely high or extremely low GDP and urbanization index values, and human activities are the primary cause of poor GWQ in JHP, potentially involving urbanization, industrial and agricultural activities, as well as fertilizer usage. Finally, the methodological framework proposed in this study is encouraged to be applied to diverse regions, such as plains, karst areas, mountainous regions, and coastal areas, to support effective future groundwater management. [Display omitted] •The spatial prediction of groundwater's entropy based WQI was showcased.•Four models were compared based on traditional DRASTIC framework.•LightGBM algorithm and SHAP analysis were employed.•Vegetation and human activity indicators have high importance. |
| ArticleNumber | 144278 |
| Author | Xiong, Hanxiang Li, Shuyi Ma, Chuanming Wang, Yuzhou Yang, Chi Li, Xiaobo Xiong, Ruihan Wang, Jinghan |
| Author_xml | – sequence: 1 givenname: Hanxiang surname: Xiong fullname: Xiong, Hanxiang email: hanxiangxiong@cug.edu.cn organization: School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China – sequence: 2 givenname: Jinghan surname: Wang fullname: Wang, Jinghan organization: School of Energy Science and Engineering, Central South University, Changsha, 410083, China – sequence: 3 givenname: Chi surname: Yang fullname: Yang, Chi organization: School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China – sequence: 4 givenname: Shuyi orcidid: 0009-0008-8503-6833 surname: Li fullname: Li, Shuyi organization: School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China – sequence: 5 givenname: Xiaobo surname: Li fullname: Li, Xiaobo organization: School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China – sequence: 6 givenname: Ruihan surname: Xiong fullname: Xiong, Ruihan organization: State Key Laboratory of Geomicrobiology and Environmental Changes, China University of Geosciences, Wuhan, 430078, China – sequence: 7 givenname: Yuzhou surname: Wang fullname: Wang, Yuzhou organization: Eastern Institute for Advanced Study, Eastern Institute of Technology, Ningbo, 315200, China – sequence: 8 givenname: Chuanming orcidid: 0000-0001-8233-8352 surname: Ma fullname: Ma, Chuanming email: machuanming@cug.edu.cn organization: School of Environmental Studies, China University of Geosciences, Wuhan, 430074, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40056819$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_asr_2025_08_031 crossref_primary_10_1016_j_engappai_2025_111843 crossref_primary_10_1080_10106049_2025_2557953 |
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| Keywords | Groundwater management Groundwater quality Human activity LightGBM Vegetation |
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| SubjectTerms | Algorithms Boosting Machine Learning Algorithms China Environmental Monitoring - methods Groundwater - chemistry Groundwater management Groundwater quality Human Activities Human activity Humans LightGBM Urbanization Vegetation Water Pollutants, Chemical - analysis Water Quality |
| Title | Critical role of vegetation and human activity indicators in the prediction of shallow groundwater quality distribution in Jianghan Plain with LightGBM algorithm and SHAP analysis |
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