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Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...

Modeling and Similitude

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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...

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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.

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Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...

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Updated: Jul 28, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework

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Published on: July 24, 2016

探索基于相似性搜索的数据驱动框架,用于多步前进的洪水预测.

Kangling Lin¹, Hua Chen¹, Yanlai Zhou¹

¹State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China.

The Science of the total environment

|May 28, 2023

概括

此摘要是机器生成的。

这项研究介绍了一种基于相似性搜索的新型时空卷积网络编码解码器 (S-TCNED) 模型,用于准确的,多步骤的洪水预测. 通过从类似的历史事件中学习,S-TCNED模型提高了对大洪水的可靠性,即使是在极端条件下.

关键词:

编码器-解码器 (ED) 的使用预测洪水可能会发生.降雨与下水的关系是下雨与下水的关系.寻找相似性的搜索时间卷积网络 (TCN)

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相关实验视频

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Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

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科学领域:

水文和水资源水文与水资源
环境科学中的人工智能
数据驱动的预测建模

背景情况:

精确的洪水预测,特别是对于大型事件和扩展的地平线,仍然是人工神经网络的挑战,因为数据有限.
当预测地平线超过流域度时间时,现有的模型扎.

研究的目的:

提出和评估一个新的基于相似性搜索的数据驱动框架,用于多步前进的洪水预测.
提高大洪水和延长预测时间的洪水预报的可靠性和准确性.
将拟议模型的性能与传统的时间卷积网络编码解码器 (TCNED) 模型进行比较.

主要方法:

开发了一个基于相似性搜索的时间卷积网络编码解码器 (S-TCNED) 模型.
使用了5232个每小时的水文数据点进行培训和测试.
输入序列包括15个站的历史洪水流量和降雨数据;输出序列预测了1至16小时前的洪水预报.
将S-TCNED与传统的TCNED模型进行比较.

主要成果:

两种TCNED和S-TCNED模型都提供了合适的多步前进洪水预测.
在模拟长期降雨-流水关系方面,S-TCNED表现出卓越的性能.
与TCNED相比,S-TCNED为大洪水提供了更可靠和更准确的预测,特别是在极端天气条件下.
在较长的预测时间 (13-16小时) 中,观察到改善的样本标签密度和Nash-Sutcliffe效率 (NSE) 之间的S-TCNED显著正相关性.

结论:

通过从类似的历史洪水发展过程中学习,S-TCNED模型有效地提高了洪水预报的可靠性和准确性.
类似性搜索机制允许有针对性的学习,提高性能,特别是长时间的预测时间.
拟议的框架有效地延长了可靠的洪水预报时间.