影响因子：2.8
DOI码：10.1016/j.jher.2024.08.002
所属单位：哈尔滨工程大学船舶工程学院
发表刊物：Journal of Hydro-environment Research
刊物所在地：荷兰
关键字：Long Short-Term Memory Network; Runoff prediction; Improved Gray Wolf Optimization Algorithm; Emergency management; Flood warning systems; Hydro-logical research
摘要：With the acceleration of global climate change and urbanization, the frequency and impact of flood disasters are increasing year by year, making flood emergency management increasingly crucial for safeguarding people’s lives, property, and societal stability. To enhance the accuracy of river flow prediction, this study employs an Improved Gray Wolf Optimization Algorithm (IGWO) to optimize parameters of the Long Short-Term Memory Network (LSTM) model. Experimental results demonstrate that the proposed algorithm significantly improves the accuracy of river flow prediction, achieving higher precision and better generalization compared to traditional machine learning algorithms. This method provides more reliable data support for flood warning systems, aiding in the accurate prediction of flood occurrence timing and intensity, thereby providing scientific basis for flood prevention and mitigation efforts. Moreover, this approach supports hydro-logical research, enhancing understanding of river water cycle processes and ecosystem changes.
合写作者：林慧,范国峰,Hsin-Pou Huang
第一作者：彭丽玲
论文类型：期刊论文
通讯作者：洪维强
学科门类：工学
文献类型：J
卷号：56
页面范围：28-39
ISSN号：1570-6443
是否译文：否
发表时间：2024-01-01
收录刊物：SCI
发布期刊链接：https://www.sciencedirect.com/science/article/abs/pii/S157064432400042X?via%3Dihub