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| DOI：10.13522/j.cnki.ggps.2026018 |
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| Predicting groundwater level in the Yinchuan Plain using stacking ensemble machine learning methods |
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Ding Pengpeng, Wang Sutong, Guo Zheng, Yang Xi, Tang Lijun, Guan Hong
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1. School of Civil and Hydraulic Engineering, Ningxia University, Yinchuan 750021, China; 2. Key Laboratory of Digital Water Management for Yellow River Water Networking, Yinchuan 750021, China; 3. Ningxia Survey and Monitoring Institute of Land and Resources, Yinchuan 750002, China; 4. Ningxia Technical College of Wine and Desertification Prevention, Yinchuan 750199, China
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| Abstract: |
| 【Objective】Groundwater is a main water resource in the Yinchuan Plain, and its dynamics is controlled by complex interactions of various natural and anthropogenic activities. This paper proposes a method to predict groundwater level changes in the plain.【Method】The model was developed based on 33 years of field-monitored data, including groundwater level, air temperature, precipitation, and evapotranspiration across the plain. The optimal lag order of groundwater level was determined using the partial autocorrelation function and time-series mutual information method. Five machine learning models, including support vector machine regression (SVR), random forest (RF), artificial neural network (ANN), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost), were constructed to predict groundwater level dynamics. Based on the prediction results of each of the five models, a stacking ensemble model was established using the Ridge regression as the meta-learner to integrate outputs of the five single models for simulating groundwater level variation in the plain.【Result】The optimal groundwater level lag order for the monitored datasets ranged from 6 to 8. The proposed ensemble model was significantly superior to the five individual standalone models, achieving an average coefficient of determination of 0.933, mean absolute error of 0.357 m, and root mean square error of 0.500 m; the average residual distribution range and average residual mean were 1.393 m and 0.006 m, respectively.【Conclusion】The model we developed based on the multi-time-lag characteristics of groundwater levels and the Stacking ensemble strategy effectively improved the accuracy and stability of groundwater level prediction in the Yinchuan Plain. |
| Key words: groundwater level prediction; machine learning; stacking algorithm; optimal lag; Yinchuan Plain |
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