基于 IGWOLSTM 模型的径流预测:实现准确的洪水预报和应急管理

IF 2.4 3区 环境科学与生态学 Q2 ENGINEERING, CIVIL
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引用次数: 0

摘要

随着全球气候变化和城市化进程的加快,洪水灾害的发生频率和影响范围逐年增大,洪水应急管理对保障人民生命财产安全和社会稳定的重要性日益凸显。为了提高河流流量预测的准确性,本研究采用改进的灰狼优化算法(IGWO)来优化长短期记忆网络(LSTM)模型的参数。实验结果表明,与传统的机器学习算法相比,所提出的算法显著提高了河流流量预测的准确性,实现了更高的精度和更好的泛化。该方法为洪水预警系统提供了更可靠的数据支持,有助于准确预测洪水发生的时间和强度,从而为防洪减灾工作提供科学依据。此外,这种方法还有助于水文逻辑研究,加深对河流水循环过程和生态系统变化的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Runoff prediction based on the IGWOLSTM model: Achieving accurate flood forecasting and emergency management

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.

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来源期刊
Journal of Hydro-environment Research
Journal of Hydro-environment Research ENGINEERING, CIVIL-ENVIRONMENTAL SCIENCES
CiteScore
5.80
自引率
0.00%
发文量
34
审稿时长
98 days
期刊介绍: The journal aims to provide an international platform for the dissemination of research and engineering applications related to water and hydraulic problems in the Asia-Pacific region. The journal provides a wide distribution at affordable subscription rate, as well as a rapid reviewing and publication time. The journal particularly encourages papers from young researchers. Papers that require extensive language editing, qualify for editorial assistance with American Journal Experts, a Language Editing Company that Elsevier recommends. Authors submitting to this journal are entitled to a 10% discount.
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