Prediction of Groundwater Level and its Correlation with Land Subsidence and Groundwater Quality in Cangzhou, North China Plain, Using Time-Series Long Short-Term Memory Neural Network and Hybrid Models
文献类型：期刊论文
| 作者 | Nafouanti, Mouigni Baraka5; Li, Junxia4,5; Chakira, Hamada3; Nyakilla, Edwin E.2; Fabiani, Denice Cleophace5; Gondwe, Jane Ferah1; Sallah, Ismaila5 |
| 刊名 | NATURAL RESOURCES RESEARCH
 |
| 出版日期 | 2025-03-07 |
| 页码 | 22 |
| 关键词 | Groundwater fluoride Groundwater level Hybrid models LSTM Land subsidence |
| ISSN号 | 1520-7439 |
| DOI | 10.1007/s11053-025-10474-1 |
| 英文摘要 | Groundwater is the primary source of drinking water in the world, but its contamination and reduction cause environmental problems. Traditional hydraulic and numerical models for assessing groundwater and land subsidence are time-consuming and expensive. Thus, this study used the long short-term memory (LSTM) neural network to predict groundwater level and employed linear regression analysis and the hybrid random forest linear regression to find the correlation between groundwater and land subsidence. The impact of groundwater level on groundwater quality was investigated by forecasting the fluoride in groundwater using the hybrid models of random forest and k-nearest neighbor (RF-KNN), random forest linear model (HRFLM), and gradient boosting support vector regression (GBR-SVR) for the prediction of groundwater fluoride. The LSTM model yielded an R2 of 0.96 in forecasting groundwater level, and the time series results from 2018 to 2022 showed a variation in groundwater level, with a decline in 2022. The LSTM model suggested that from 2024 to 2040, the groundwater level would recover progressively. The regression analysis showed an R2 of 0.99 and a p value of 0.01 for the correlation between groundwater level and land subsidence, and the HRFLM model yielded an R2 of 0.94. For predicting groundwater fluoride contamination, the hybrid RF-KNN had the highest R2 of 0.97 compared to HRFLM and GBR-SVR, with R2 of 0.95 and 0.93, respectively. This research demonstrated that hybrid models and deep learning are advanced techniques that can be applied in Cangzhou to evaluate groundwater level and land subsidence and they can be applied in areas facing similar challenges. |
| 资助项目 | This work was supported by the National Natural Science Foundation of China[42020104005] ; This work was supported by the National Natural Science Foundation of China[42277067] ; National Natural Science Foundation of China ; Chinese Government Scholarship (CSC) |
| WOS研究方向 | Geology |
| 语种 | 英语 |
| WOS记录号 | WOS:001439053200001 |
| 出版者 | SPRINGER |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/36789]  |
| 专题 | 中科院武汉岩土力学所 |
| 通讯作者 | Li, Junxia |
| 作者单位 | 1.China Univ Geosci, Sch Geog, Wuhan 430074, Peoples R China 2.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China 3.Univ Comoros, Dept Sci & Technol, Moroni, Comoros 4.China Univ Geosci, MOE Key Lab Groundwater Qual & Hlth, Wuhan 430078, Peoples R China 5.China Univ Geosci, Sch Environm Studies, Wuhan 430074, Peoples R China |
| 推荐引用方式 GB/T 7714 | Nafouanti, Mouigni Baraka,Li, Junxia,Chakira, Hamada,et al. Prediction of Groundwater Level and its Correlation with Land Subsidence and Groundwater Quality in Cangzhou, North China Plain, Using Time-Series Long Short-Term Memory Neural Network and Hybrid Models[J]. NATURAL RESOURCES RESEARCH,2025:22. |
| APA | Nafouanti, Mouigni Baraka.,Li, Junxia.,Chakira, Hamada.,Nyakilla, Edwin E..,Fabiani, Denice Cleophace.,...&Sallah, Ismaila.(2025).Prediction of Groundwater Level and its Correlation with Land Subsidence and Groundwater Quality in Cangzhou, North China Plain, Using Time-Series Long Short-Term Memory Neural Network and Hybrid Models.NATURAL RESOURCES RESEARCH,22. |
| MLA | Nafouanti, Mouigni Baraka,et al."Prediction of Groundwater Level and its Correlation with Land Subsidence and Groundwater Quality in Cangzhou, North China Plain, Using Time-Series Long Short-Term Memory Neural Network and Hybrid Models".NATURAL RESOURCES RESEARCH (2025):22. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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