用于流域尺度水文模型优化的遗传算法新型降雨分布方法

IF 2.2 3区 工程技术 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Charalampos Skoulikaris, Nikolaos Nagkoulis
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The research proposes a methodological framework where a genetic algorithm (GA)-based method responsible for the spatial distribution of gauge observations at the basin scale is coupled with the HEC-HMS hydrological model to produce simulated discharges of high accuracy. The custom-developed GA is used to divide a 2D space, adhering to specific criteria, into polygonal geometries that represent gauge zones of influences, similar to the Thiessen polygon method concept. A collection of vectorial polygonal areas, equivalent in number to the employed monitoring stations, is produced with the areal weights to be used for distributing the rainfall across the case study basin and subsequently to force the hydrological simulations. The generated gauge weights are validated for a different temporal precipitation event. The final outputs expressed through a series of statistical measures, clearly demonstrate the effectiveness of the specific methodology (e.g. <em>R</em><sup>2</sup> and Nash–Sutcliffe are larger than 0.83 and 0.73). The methodology can foster accurate hydrological simulations, especially in cases where there is a limited number of rainfall stations and corresponding observations.</p>","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A genetic algorithm's novel rainfall distribution method for optimized hydrological modeling at basin scales\",\"authors\":\"Charalampos Skoulikaris, Nikolaos Nagkoulis\",\"doi\":\"10.2166/hydro.2024.224\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div data- reveal-group-><div><img alt=\\\"graphic\\\" data-src=\\\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/6/10.2166_hydro.2024.224/1/m_hydro-d-23-00224gf01.png?Expires=1722776541&amp;Signature=WUW83ZFchbelEpC~92sxSdnRMa9uT2LOf9eonalq81ChFe-N-j-Vqx-JFPu3xykBHFSaO67QRTHfWGOnXXxzOKJluLqPpyK0~F~RUa20tz~x7BvayLefVcuLsw8nIY~YgCUMzs-U1XcZZbq92bL7EEjluqvefzIOp-f4dFwHsBwYS89zE-QGkjkVF58vi8CXv5U6Aj7lmy-1GPTdBtBNp4~IU1yp5qctP5q4vaDmmVCGdV8YAEqSHCTYzn6JXYWmtmLywQjOtixtgtePULNM-IPmUr7RFhvYfO5rJMtiEUVvD7OYfy7EaRX~8uP2HnwmjgvViRI5xmThUkBioXfWXA__&amp;Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\\\" path-from-xml=\\\"hydro-d-23-00224gf01.tif\\\" src=\\\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/6/10.2166_hydro.2024.224/1/m_hydro-d-23-00224gf01.png?Expires=1722776541&amp;Signature=WUW83ZFchbelEpC~92sxSdnRMa9uT2LOf9eonalq81ChFe-N-j-Vqx-JFPu3xykBHFSaO67QRTHfWGOnXXxzOKJluLqPpyK0~F~RUa20tz~x7BvayLefVcuLsw8nIY~YgCUMzs-U1XcZZbq92bL7EEjluqvefzIOp-f4dFwHsBwYS89zE-QGkjkVF58vi8CXv5U6Aj7lmy-1GPTdBtBNp4~IU1yp5qctP5q4vaDmmVCGdV8YAEqSHCTYzn6JXYWmtmLywQjOtixtgtePULNM-IPmUr7RFhvYfO5rJMtiEUVvD7OYfy7EaRX~8uP2HnwmjgvViRI5xmThUkBioXfWXA__&amp;Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\\\"/><div>View largeDownload slide</div></div></div><div content- data-reveal=\\\"data-reveal\\\"><div><img alt=\\\"graphic\\\" data-src=\\\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/6/10.2166_hydro.2024.224/1/m_hydro-d-23-00224gf01.png?Expires=1722776541&amp;Signature=WUW83ZFchbelEpC~92sxSdnRMa9uT2LOf9eonalq81ChFe-N-j-Vqx-JFPu3xykBHFSaO67QRTHfWGOnXXxzOKJluLqPpyK0~F~RUa20tz~x7BvayLefVcuLsw8nIY~YgCUMzs-U1XcZZbq92bL7EEjluqvefzIOp-f4dFwHsBwYS89zE-QGkjkVF58vi8CXv5U6Aj7lmy-1GPTdBtBNp4~IU1yp5qctP5q4vaDmmVCGdV8YAEqSHCTYzn6JXYWmtmLywQjOtixtgtePULNM-IPmUr7RFhvYfO5rJMtiEUVvD7OYfy7EaRX~8uP2HnwmjgvViRI5xmThUkBioXfWXA__&amp;Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\\\" path-from-xml=\\\"hydro-d-23-00224gf01.tif\\\" src=\\\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/6/10.2166_hydro.2024.224/1/m_hydro-d-23-00224gf01.png?Expires=1722776541&amp;Signature=WUW83ZFchbelEpC~92sxSdnRMa9uT2LOf9eonalq81ChFe-N-j-Vqx-JFPu3xykBHFSaO67QRTHfWGOnXXxzOKJluLqPpyK0~F~RUa20tz~x7BvayLefVcuLsw8nIY~YgCUMzs-U1XcZZbq92bL7EEjluqvefzIOp-f4dFwHsBwYS89zE-QGkjkVF58vi8CXv5U6Aj7lmy-1GPTdBtBNp4~IU1yp5qctP5q4vaDmmVCGdV8YAEqSHCTYzn6JXYWmtmLywQjOtixtgtePULNM-IPmUr7RFhvYfO5rJMtiEUVvD7OYfy7EaRX~8uP2HnwmjgvViRI5xmThUkBioXfWXA__&amp;Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\\\"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>Rainfall has a dominant role in rainfall-runoff models, with the rendering of these models depending on the data accuracy and on the way that rainfall is spatially allocated. 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引用次数: 0

摘要

查看大尺寸下 载幻灯片查看大尺寸下 载幻灯片 关闭模态降雨在降雨-径流模型中起着主导作用,这些模型的效果取决于数据精度和降雨的空间分配方式。本研究提出了一种方法框架,即基于遗传算法(GA)的方法与 HEC-HMS 水文模型相结合,生成高精度的模拟排水量。定制开发的遗传算法用于按照特定标准将二维空间划分为多边形几何图形,这些几何图形代表测量影响区,与 Thiessen 多边形方法的概念类似。生成的矢量多边形区域集合在数量上等同于所采用的监测站,其区域权重将用于分配案例研究流域的降雨量,并随后强制进行水文模拟。生成的测站权重在不同时间的降水事件中得到验证。通过一系列统计指标得出的最终结果清楚地表明了特定方法的有效性(例如,R2 和 Nash-Sutcliffe 分别大于 0.83 和 0.73)。该方法可以促进精确的水文模拟,尤其是在雨量站和相应观测数据数量有限的情况下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A genetic algorithm's novel rainfall distribution method for optimized hydrological modeling at basin scales
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Rainfall has a dominant role in rainfall-runoff models, with the rendering of these models depending on the data accuracy and on the way that rainfall is spatially allocated. The research proposes a methodological framework where a genetic algorithm (GA)-based method responsible for the spatial distribution of gauge observations at the basin scale is coupled with the HEC-HMS hydrological model to produce simulated discharges of high accuracy. The custom-developed GA is used to divide a 2D space, adhering to specific criteria, into polygonal geometries that represent gauge zones of influences, similar to the Thiessen polygon method concept. A collection of vectorial polygonal areas, equivalent in number to the employed monitoring stations, is produced with the areal weights to be used for distributing the rainfall across the case study basin and subsequently to force the hydrological simulations. The generated gauge weights are validated for a different temporal precipitation event. The final outputs expressed through a series of statistical measures, clearly demonstrate the effectiveness of the specific methodology (e.g. R2 and Nash–Sutcliffe are larger than 0.83 and 0.73). The methodology can foster accurate hydrological simulations, especially in cases where there is a limited number of rainfall stations and corresponding observations.

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来源期刊
Journal of Hydroinformatics
Journal of Hydroinformatics 工程技术-工程:土木
CiteScore
4.80
自引率
3.70%
发文量
59
审稿时长
3 months
期刊介绍: Journal of Hydroinformatics is a peer-reviewed journal devoted to the application of information technology in the widest sense to problems of the aquatic environment. It promotes Hydroinformatics as a cross-disciplinary field of study, combining technological, human-sociological and more general environmental interests, including an ethical perspective.
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