In August 2020, following a prolonged drought and intense rainfall, a dam near the Seomjin River in Korea experienced overflow during a water release, resulting in damages exceeding 100 billion won (USD 76 million). The flooding was attributed to maintaining the dam’s water level 6 meters higher than the norm. Could this incident have been averted through predictive dam management?
A research team led by Professor Jonghun Kam and Eunmi Lee, a PhD candidate from the Division of Environmental Science & Engineering at Pohang University of Science and Technology (POSTECH), recently employed deep learning techniques to scrutinize dam operation patterns and assess their effectiveness. Their findings were published in the Journal of Hydrology.
Korea faces a precipitation peak during the summer, relying on dams and associated infrastructure for water management. However, the escalating global climate crisis has led to unforeseen typhoons and droughts, complicating dam operations.
A new study has emerged. It aims to surpass conventional physical models by harnessing the potential of an artificial intelligence (AI) model trained on extensive big data.
The team focused on crafting an AI model aimed at predicting the operational patterns of dams within the Seomjin River basin. They specifically focused on the Seomjin River, Juam, and Juam Control Dam. The team also wanted to understand the decision-making processes of the trained AI models.
Predictive dam management from deep learning
Their objective was to formulate a scenario outlining the methodology for forecasting dam water levels. Employing the Gated Recurrent Unit (GRU) model, a deep learning algorithm, the team trained it using data from 2002 to 2021 from dams along the Seomjin River. Precipitation, inflow, and outflow data were inputs, while hourly dam levels served as outputs. The analysis demonstrated remarkable accuracy, boasting an efficiency index exceeding 0.9.
Subsequently, the team devised explainable scenarios. They manipulated inputs by -40 per cent, -20 per cent, +20 per cent, and 40 per cent of each input variable. The goal was to examine how the trained GRU model responded to these input alterations. While changes in precipitation had a negligible impact on dam water levels, variations in inflow significantly influenced the dam’s water level. Notably, the identical change in outflow yielded different water levels at distinct barriers, affirming that the GRU model had effectively learned the unique operational nuances of each dam.
Professor Jonghun Kam remarked, “Our examination delved beyond predicting the patterns of dam operations to securitize their effectiveness using AI models. We introduced a methodology to indirectly understand the decision-making process of an AI-based black box model determining dam water levels.”
He further stated, “Our aspiration is that this insight will contribute to a deeper understanding of dam operations and enhance their efficiency in the future.”
The study was published in the Journal of Hydrology.
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