日本地球惑星科学連合2025年大会

講演情報

[E] ポスター発表

セッション記号 A (大気水圏科学) » A-HW 水文・陸水・地下水学・水環境

[A-HW22] River Channel Morphology, Water Resource Management, and Advanced Techniques

2025年5月27日(火) 17:15 〜 19:15 ポスター会場 (幕張メッセ国際展示場 7・8ホール)

コンビーナ:Huang Cheng-Chia(Feng Chia University)、HU Ming-Che(National Taiwan University)、木村 匡臣(近畿大学)、Lee Fong-Zuo(National Chung Hsing University)

17:15 〜 19:15

[AHW22-P04] Integrating Deep Learning and Explainable AI for Groundwater Level Prediction in Taiwan

*Yun-Ting Wang1Pu-Yun Kow1、Jia-Yi Liou1、Fi-John Chang1 (1.Department of Bioenvironmental Systems Engineering, National Taiwan University)

キーワード:Groundwater forecasting, deep learning, explainable AI, Taiwan

Climate change has profoundly impacted global water resources, intensifying extreme events like droughts and floods, underscoring the need for effective groundwater management. Groundwater level prediction involves complex hydrological and climatic dynamics and is also influenced by human activities like groundwater pumping. Traditional methods, such as numerical models and empirical formulas, face significant challenges in accurately simulating groundwater levels due to the highly nonlinear nature of hydro-meteorological systems. In Taiwan, the Zhuoshui River basin, as one of the country’s primary irrigation zones, relies heavily on surface water from the Zhuoshui River and its underlying groundwater resources. Efficient groundwater allocation, supported by reliable predictive tools, is critical to sustainable river basin management, which serves as a major agricultural hub.

This study utilizes 10 years (2012–2022) of hydrological and anthropogenic activity data, including groundwater levels, river water levels, and pumps’ electricity usage, to develop a multi-input, multi-output, multi-horizon (MIMOMH) deep learning framework for 1- to 3-month ahead groundwater level prediction. For sustainable river basin management, Explainable AI (XAI) techniques further provide deeper insights into the feature importance of input variables within the proposed framework, particularly from the perspectives of individual groundwater and flow monitoring stations as well as groundwater pumps. This research is expected to advance groundwater level prediction and address critical resource management challenges by providing a replicable framework applicable to Taiwan and similar regions.