Streamflow prediction using Long Short-term Memory networks

Author:

Nguyen Nhu Y1ORCID,Kha Dang Dinh2,Ninh Luu Van2,Anh Tran Ngoc2

Affiliation:

1. VNU University of Science Faculty of Hydrology Meteorology and Oceanography

2. VNU University of Science

Abstract

Abstract Accurate river streamflow prediction is crucial for hydropower operations, agricultural planning, and effective water resources management. However, forecasting reliable streamflow poses challenges due to the intricate nature of weather patterns and non-linear runoff generation mechanisms. The long short-term memory (LSTM) network has gained prominence for effectively simulating non-linear patterns. Despite its popularity, the performance of LSTM in river flow prediction remains insufficiently understood. This study assesses LSTM's effectiveness and explores how different network structures and hyperparameters impact short-term daily streamflow prediction at Kratie stations, a vital hydrological site in the Vietnam Mekong Delta. Training LSTM on historical streamflow data, we find that the size of the training dataset significantly influences network training, recommending a dataset spanning 2013 to 2022 for optimal results. Incorporating a hidden layer with a non-linear activation function enhances learning efficiency, and adding a fully connected layer slightly improves prediction ability. Careful tuning of parameters such as epochs, dropout, and the number of LSTM units enhances predictive accuracy. The stacked LSTM with sigmoid activation stands out, demonstrating excellent performance with a high Nash–Sutcliffe Efficiency (NSE) of 0.95 and a low root relative mean square error (rRMSE) of approximately 0.002%. Moreover, the model excels in forecasting streamflow for 5 to 15 antecedent days, with five days exhibiting particularly high accuracy.

Publisher

Research Square Platform LLC

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3