Comparison of Hybrid LSTAR-GARCH Model with Conventional Stochastic and Artificial-Intelligence Models to Estimate Monthly Streamflow

IF 3.9 3区环境科学与生态学 Q1 ENGINEERING, CIVIL

Water Resources Management Pub Date : 2024-04-03 DOI:10.1007/s11269-024-03834-8

Priyanka Sharma, F. Fathian, D. Machiwal, S. R. Bhakar, Survey D. Sharma

引用次数: 0

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混合 LSTAR-GARCH 模型与传统随机模型和人工智能模型在估算月度流量方面的比较

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Water Resources Management 环境科学-工程：土木

CiteScore

7.40

自引率

16.30%

发文量

332

审稿时长

9 months

期刊介绍： Water Resources Management is an international, multidisciplinary forum for the publication of original contributions and the exchange of knowledge and experience on the management of water resources. In particular, the journal publishes contributions on water resources assessment, development, conservation and control, emphasizing policies and strategies. Contributions examine planning and design of water resource systems, and operation, maintenance and administration of water resource systems. Coverage extends to these closely related topics: water demand and consumption; applied surface and groundwater hydrology; water management techniques; simulation and modelling of water resource systems; forecasting and control of quantity and quality of water; economic and social aspects of water use; legislation and water resources protection. Water Resources Management is supported scientifically by the European Water Resources Association, a scientific and technical nonprofit-making European association.