用于绘制河道洪水淹没图的新型多模型集合框架

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Nikunj K. Mangukiya , Shashwat Kushwaha , Ashutosh Sharma
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引用次数: 0

摘要

洪水对社区和基础设施构成重大威胁,需要及时预测,以便进行有效的灾害管理。传统的水动力模型往往在数据要求和计算效率方面受到限制。为了克服这些限制,我们提出了一种新颖的多模型集合框架,将洪水范围和深度模型整合在一起,用于绘制河道洪水图。考虑到地形高程和坡度、流向、与河流的距离以及经纬度等各种洪水条件因素的相关性,我们选择了这些因素作为模型输入。对所提出的框架进行了预测、推断和概括能力评估。结果表明,所提出的模型成功地捕捉到了包括意外事件在内的各种溪流值的洪水动态,使其成为预测洪水范围和深度的重要工具。总之,我们的方法为传统的水动力模型提供了一种有前途的替代方案,具有稳健性、计算效率、可扩展性、自动化以及与现有洪水淹没绘图任务工具的集成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel multi-model ensemble framework for fluvial flood inundation mapping

Floods pose a significant threat to communities and infrastructure, necessitating timely predictions for effective disaster management. Conventional hydrodynamic models often encounter limitations in data requirements and computational efficiency. To overcome these constraints, we propose a novel multi-model ensemble framework integrating the flood extent and depth models for fluvial flood mapping. Various flood conditioning factors, such as terrain elevation and slope, flow direction, distance from the river, and latitude-longitude, were selected as model inputs, considering their relevance. The proposed framework was evaluated for predictive, extrapolative, and generalization capabilities. Results indicate that the proposed model successfully captures flood dynamics across a wide range of streamflow values, including unforeseen events, making it a valuable tool for predicting flood extent and depth. Overall, our approach offers a promising alternative to conventional hydrodynamic models, providing robustness, computational efficiency, scalability, automation, and integration with existing tools for flood inundation mapping tasks.

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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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