Bayesian Model Averaging Ensemble Approach for Multi-Time-Ahead Groundwater Level Prediction Combining the GRACE, GLEAM, and GLDAS Data in Arid Areas

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 1; p. 188
• Main Authors: Zhou, Ting, Wen, Xiaohu, Feng, Qi, Yu, Haijiao, Xi, Haiyang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.01.2023
• Subjects: Accuracy; Agriculture; Aquifers; Artificial neural networks; Bayesian analysis; Bayesian model averaging; Climate change; Data collection; Decision making; Decision trees; Evaporation; Geology; GLDAS; GLEAM; GRACE; GRACE (experiment); Gravity; Groundwater; groundwater level; Groundwater levels; Hydrogeology; Hydrology; Investigations; Learning algorithms; Machine learning; Mathematical models; Meteorological data; Missing data; Ordinary differential equations; Performance evaluation; Precipitation; Predictions; Regions; Reliability analysis; Remote sensing; Satellites; Simulation; Stream flow; Support vector machines; Sustainability management; Uncertainty analysis; Water resources; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15010188

Accurate groundwater level (GWL) prediction is essential for the sustainable management of groundwater resources. However, the prediction of GWLs remains a challenge due to insufficient data and the complicated hydrogeological system. In this study, we investigated the ability of the Gravity Recovery and Climate Experiment (GRACE) satellite data, the Global Land Evaporation Amsterdam Model (GLEAM) data, the Global Land Data Assimilation System (GLDAS) data, and the publicly available meteorological data in 1-, 2-, and 3-month-ahead GWL prediction using three traditional machine learning models (extreme learning machine, ELM; support vector machine, SVR; and random forest, RF). Meanwhile, we further developed the Bayesian model averaging (BMA) by combining the ELM, SVR, and RF models to avoid the uncertainty of the single models and to improve the predicting accuracy. The validity of the forcing data and the BMA model were assessed for three GWL monitoring wells in the Zhangye Basin in Northwest China. The results indicated that the applied forcing data could be treated as validated inputs to predict the GWL up to 3 months ahead due to the achieved high accuracy of the machine learning models (NS > 0.55). The BMA model could significantly improve the performance of the single machine learning models. Overall, the BMA model reduced the RMSE of the ELM, SVR, and RF models in the testing period by about 13.75%, 24.01%, and 17.69%, respectively; while it improved the NS by about 8.32%, 16.13%, and 9.67% for 1-, 2-, and 3-month-ahead GWL prediction, respectively. The uncertainty analysis results also verified the reliability of the BMA model in multi-time-ahead GWL predicting. This highlighted the efficiency of the satellite data, satellite-based data, and publicly available data as substitute inputs in machine-learning-based GWL prediction, particularly for areas with insufficient or missing data. Meanwhile, the BMA ensemble strategy can serve as a powerful and reliable approach in multi-time-ahead GWL prediction when risk-based decision making is needed or a lack of relevant hydrogeological data impedes the application of the physical models.