Identifying Hydrometeorological Factors Influencing Reservoir Releases Using Machine Learning Methods

Ming Fan, Lujun Zhang, Siyan Liu, Tiantian Yang, Dawei Lu
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引用次数: 4

Abstract

Simulation of reservoir releases plays a critical role in social-economic functioning and our nation's security. How-ever, it is challenging to predict the reservoir release accurately because of many influential factors from natural environments and engineering controls such as the reservoir inflow and storage. Moreover, climate change and hydrological intensification causing the extreme precipitation and temperature make the accurate prediction of reservoir releases even more challenging. Machine learning (ML) methods have shown some successful applications in simulating reservoir releases. However, previous studies mainly used inflow and storage data as inputs and only considered their short-term influences (e.g, previous one or two days). In this work, we use long short-term memory (LSTM) networks for reservoir release prediction based on four input variables including inflow, storage, precipitation, and temperature and consider their long-term influences. We apply the LSTM model to 30 reservoirs in Upper Colorado River Basin, United States. We analyze the prediction performance using six statistical metrics. More importantly, we investigate the influence of the input hydrometeorological factors, as well as their temporal effects on reservoir release decisions. Results indicate that inflow and storage are the most influential factors but the inclusion of precipitation and temperature can further improve the prediction of release especially in low flows. Additionally, the inflow and storage have a relatively long-term effect on the release. These findings can help optimize the water resources management in the reservoirs.
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利用机器学习方法识别影响水库释放的水文气象因素
水库放水模拟对社会经济运行和国家安全具有重要意义。然而,由于受自然环境和工程控制因素的影响,如水库入库、库容等,对水库释放量的准确预测具有一定的挑战性。此外,气候变化和水文加剧导致极端降水和温度,使水库释放的准确预测更具挑战性。机器学习(ML)方法在模拟油藏释放方面已经取得了一些成功的应用。然而,以往的研究主要使用流入和储存数据作为输入,只考虑它们的短期影响(如前一天或两天)。在这项工作中,我们使用长短期记忆(LSTM)网络进行水库释放预测,该预测基于四个输入变量,包括流入、储存、降水和温度,并考虑它们的长期影响。将LSTM模型应用于美国上科罗拉多河流域的30个水库。我们使用六个统计指标来分析预测性能。更重要的是,我们研究了输入水文气象因子的影响,以及它们对水库放水决策的时间效应。结果表明,入水量和库存量是影响最大的因素,但降水和温度的加入可以进一步改善对释放量的预测,尤其是在低流量的情况下。此外,流入和储存对释放有相对长期的影响。这些发现有助于水库水资源管理的优化。
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