Prediction of Groundwater Level Based on the Integration of Electromagnetic Induction, Satellite Data, and Artificial Intelligent
Groundwater level (GWL) in dry areas is an important parameter for understanding groundwater resources and environmental sustainability. Remote sensing data combined with machine learning algorithms have become one of the important tools for groundwater level modeling. However, the effectiveness of...
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/210 |
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| author | Fei Wang Lili Han Lulu Liu Yang Wei Xian Guo |
| author_facet | Fei Wang Lili Han Lulu Liu Yang Wei Xian Guo |
| author_sort | Fei Wang |
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| description | Groundwater level (GWL) in dry areas is an important parameter for understanding groundwater resources and environmental sustainability. Remote sensing data combined with machine learning algorithms have become one of the important tools for groundwater level modeling. However, the effectiveness of the above-based model in the plains of the arid zone remains underexplored. Fortunately, soil salinity and soil moisture may provide an optimized solution for GWL prediction based on the application of apparent conductivity (ECa, mS/m) using electromagnetic induction (EMI). This has not been attempted in previous studies in oases in arid regions. The study proposed two strategies to predict GWL, included an ECa-based GWL model and a remote sensing-based GWL model (RS_GWL), and then compared and explored their performances and cooperation possibilities. To this end, this study first constructed the ECa prediction model and the RS_GWL with ensemble machine learning algorithms using environmental variables and field observations (474 ECa reads and 436 groundwater level observations from a mountain–oasis–desert system, respectively). Subsequently, a strategy to improve the prediction accuracy of GWL was proposed by comparing the correlation between GWL observations and the two models. The results showed that the RS_GWL prediction model explains 30% and 90% of the spatial variability in the two value domain intervals, GWL < 10 m and GWL > 10 m, respectively. The R<sup>2</sup> of the modeling and the validation of the ECa was 79% and 73%, respectively. Careful analysis of the scatter plots between predicted ECa and GWL revealed that when ECa varies between 0–600 mS/m, 600–800 mS/m, 800–1100 mS/m, and >1100 mS/m, the fluctuation ranges of the corresponding GWL correspond to 0–31 m, 0–15 m, 0–10 m, and 0–5 m. Finally, combining the spatial variability of ECa and RS_GWL spatial distribution map, the following optimization strategies were finally established: GWL < 5 m (in natural land with ECa > 1100 mS/m), GWL < 5 m (occupied by farmland from RS_GWL) and GWL > 10 m (from RS_GWL), and 3 < GWL < 10 m (speculated). In conclusion, this study has demonstrated that the integration of EMI technology has significantly improved the precision of forecasting shallow GWL in oasis plain regions, outperforming the outcomes achieved by the use of remote sensing data alone. |
| format | Article |
| id | doaj-art-7bf793ca1046467c83505ba1cc0b89e9 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-7bf793ca1046467c83505ba1cc0b89e92025-01-24T13:47:44ZengMDPI AGRemote Sensing2072-42922025-01-0117221010.3390/rs17020210Prediction of Groundwater Level Based on the Integration of Electromagnetic Induction, Satellite Data, and Artificial IntelligentFei Wang0Lili Han1Lulu Liu2Yang Wei3Xian Guo4School of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, ChinaSchool of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, ChinaSchool of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, ChinaSchool of Architecture and Civil Engineering, Chengdu University, Chengdu 610106, ChinaSchool of Earth Resources, China University of Geosciences, Wuhan 430074, ChinaGroundwater level (GWL) in dry areas is an important parameter for understanding groundwater resources and environmental sustainability. Remote sensing data combined with machine learning algorithms have become one of the important tools for groundwater level modeling. However, the effectiveness of the above-based model in the plains of the arid zone remains underexplored. Fortunately, soil salinity and soil moisture may provide an optimized solution for GWL prediction based on the application of apparent conductivity (ECa, mS/m) using electromagnetic induction (EMI). This has not been attempted in previous studies in oases in arid regions. The study proposed two strategies to predict GWL, included an ECa-based GWL model and a remote sensing-based GWL model (RS_GWL), and then compared and explored their performances and cooperation possibilities. To this end, this study first constructed the ECa prediction model and the RS_GWL with ensemble machine learning algorithms using environmental variables and field observations (474 ECa reads and 436 groundwater level observations from a mountain–oasis–desert system, respectively). Subsequently, a strategy to improve the prediction accuracy of GWL was proposed by comparing the correlation between GWL observations and the two models. The results showed that the RS_GWL prediction model explains 30% and 90% of the spatial variability in the two value domain intervals, GWL < 10 m and GWL > 10 m, respectively. The R<sup>2</sup> of the modeling and the validation of the ECa was 79% and 73%, respectively. Careful analysis of the scatter plots between predicted ECa and GWL revealed that when ECa varies between 0–600 mS/m, 600–800 mS/m, 800–1100 mS/m, and >1100 mS/m, the fluctuation ranges of the corresponding GWL correspond to 0–31 m, 0–15 m, 0–10 m, and 0–5 m. Finally, combining the spatial variability of ECa and RS_GWL spatial distribution map, the following optimization strategies were finally established: GWL < 5 m (in natural land with ECa > 1100 mS/m), GWL < 5 m (occupied by farmland from RS_GWL) and GWL > 10 m (from RS_GWL), and 3 < GWL < 10 m (speculated). In conclusion, this study has demonstrated that the integration of EMI technology has significantly improved the precision of forecasting shallow GWL in oasis plain regions, outperforming the outcomes achieved by the use of remote sensing data alone.https://www.mdpi.com/2072-4292/17/2/210groundwater levelsremote sensingelectromagnetic inductionmachine learningTarim Basin |
| spellingShingle | Fei Wang Lili Han Lulu Liu Yang Wei Xian Guo Prediction of Groundwater Level Based on the Integration of Electromagnetic Induction, Satellite Data, and Artificial Intelligent Remote Sensing groundwater levels remote sensing electromagnetic induction machine learning Tarim Basin |
| title | Prediction of Groundwater Level Based on the Integration of Electromagnetic Induction, Satellite Data, and Artificial Intelligent |
| title_full | Prediction of Groundwater Level Based on the Integration of Electromagnetic Induction, Satellite Data, and Artificial Intelligent |
| title_fullStr | Prediction of Groundwater Level Based on the Integration of Electromagnetic Induction, Satellite Data, and Artificial Intelligent |
| title_full_unstemmed | Prediction of Groundwater Level Based on the Integration of Electromagnetic Induction, Satellite Data, and Artificial Intelligent |
| title_short | Prediction of Groundwater Level Based on the Integration of Electromagnetic Induction, Satellite Data, and Artificial Intelligent |
| title_sort | prediction of groundwater level based on the integration of electromagnetic induction satellite data and artificial intelligent |
| topic | groundwater levels remote sensing electromagnetic induction machine learning Tarim Basin |
| url | https://www.mdpi.com/2072-4292/17/2/210 |
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