Improving Groundwater Level Prediction in the Zhuoshui River Basin, Taiwan, through Advanced Deep Learning Methods

Despite its abundant rainfall, Taiwan faces persistent water scarcity issues due to its steep terrain, rapid runoff, and uneven precipitation distribution. The limited effectiveness of reservoirs, owing to the country's small land area, has led to an increased reliance on groundwater, surpassing thirty percent of the total water usage and exceeding contributions from reservoirs. Understanding groundwater level dynamics, especially in critical regions like the central area around the Zhuoshui River, is imperative. This comprehensive study endeavors to collect data from multiple groundwater and hydrological monitoring stations, encompassing parameters such as temperature and precipitation. The collected data will undergo meticulous purification, ensuring the acquisition of high-quality information for subsequent in-depth analysis. Utilizing Long Short-Term Memory (LSTM), a deep learning module, this research aims to predict groundwater level changes. The study will identify key parameters influencing these changes by exploring the correlation of various factors with groundwater levels. The anticipated outcomes are poised to deliver valuable insights for the enhancement of planning and management strategies concerning groundwater recharge and extraction. This, in turn, will contribute to the more effective and sustainable utilization of Taiwan's groundwater resources, addressing water scarcity challenges and fostering environmental resilience in the future.