Advanced groundwater level forecasting with hybrid deep learning model: Tackling water challenges in Taiwan’s largest alluvial fan
Journal
Journal of Hydrology
Series/Report No.
Journal of Hydrology
Journal Volume
655
Start Page
132887
ISSN
0022-1694
Date Issued
2025-07
Author(s)
DOI
10.1016/j.jhydrol.2025.132887
Abstract
Groundwater is crucial for food security and economic development, yet it faces growing threats from over-extraction and extreme weather events. The Zhuoshui River alluvial fan, Taiwan's largest, has long served as a key water source. However, recent climate change and industrial expansion have significantly affected groundwater recharge and quality, contributing to land subsidence. Accurate forecasting of groundwater levels is essential to ensuring environmental sustainability in the region. This study presents a novel hybrid deep learning model, CNN-BP, which integrates Convolutional Neural Networks (CNN) with Backpropagation Neural Networks (BPNN) to forecast groundwater levels three days in advance at 25 monitoring stations across the Zhuoshui River alluvial fan. The CNN-BP model was benchmarked against a standalone BPNN model. Both models were trained on a dataset of 7,291 daily hydro-geo-meteorological records from 2000 to 2019, including groundwater levels, rainfall, streamflow, temperature, evaporation, and lithology. The study emphasizes comprehensive input selection, feature extraction, and hyperparameter tuning, with Random Forest utilized to filter input factors from 20 rainfall stations, thereby improving forecast accuracy and reliability. The CNN-BP model significantly outperformed the BPNN model, achieving R2 values between 0.94 and 0.98 across various stations and effectively mitigating time-delay issues. The study also explored the relationship between forecast errors and the fan's lithological characteristics, providing valuable insights for land-use planning and groundwater management. Validation during Typhoons Haitang and Maria further demonstrated the model's capability to predict groundwater recharge under intense rainfall conditions. By integrating environmental and social factors such as drought frequency, population density, and recharge potential, this study underscores the need for targeted water management strategies. The findings offer critical insights for future regional approaches to groundwater management, promoting sustainable practices across watersheds. Ultimately, this study serves as a valuable resource for informed decision-making in land-use planning and water resource management, advancing the sustainable utilization of groundwater in the Zhuoshui River alluvial fan.
Subjects
Convolutional Neural Network (CNN)
Cross-correlation function (CCF)
Deep learning
Groundwater recharge
Lithology
Regional groundwater forecasting
Publisher
Elsevier BV
Description
Article number: 132887
Type
journal article