<div><div data- reveal-group-><div><img alt="graphic" data-src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/2/10.2166_hydro.2024.121/1/m_hydro-d-23-00121gf01.png?Expires=1712248319&Signature=MBiDDIfBANl5gcv~tx56Jn~TjUdkd53Sw1r0lYohiyDO-cDqWQc5ATJeN9k9TlO939pkTOn3aOFWmBp8b9~0QXkMCUEqgP~cdCFUmI4K4dQ6Taixt83W3Bw0cB0nWK9esBugT~J6SXKT66kp9uK-Ajc3i~rBkRE5HMBTV9rlDJHUg0EVw4xCS0madLwQ28ON0mxSMiO~hVvztcAW5a~Mtf~U~4STezGW~AytqbRsq586E4SI19rLzn0cIa4yEh2YpY6YnH3YC7vwma3olw9VUI8oCUkU9frphzwHpR4W5B-DFlprNjVzR7-1cdWN92MCLn0350S8FiKWuYHXy1csag__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydro-d-23-00121gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/2/10.2166_hydro.2024.121/1/m_hydro-d-23-00121gf01.png?Expires=1712248319&Signature=MBiDDIfBANl5gcv~tx56Jn~TjUdkd53Sw1r0lYohiyDO-cDqWQc5ATJeN9k9TlO939pkTOn3aOFWmBp8b9~0QXkMCUEqgP~cdCFUmI4K4dQ6Taixt83W3Bw0cB0nWK9esBugT~J6SXKT66kp9uK-Ajc3i~rBkRE5HMBTV9rlDJHUg0EVw4xCS0madLwQ28ON0mxSMiO~hVvztcAW5a~Mtf~U~4STezGW~AytqbRsq586E4SI19rLzn0cIa4yEh2YpY6YnH3YC7vwma3olw9VUI8oCUkU9frphzwHpR4W5B-DFlprNjVzR7-1cdWN92MCLn0350S8FiKWuYHXy1csag__&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/26/2/10.2166_hydro.2024.121/1/m_hydro-d-23-00121gf01.png?Expires=1712248319&Signature=MBiDDIfBANl5gcv~tx56Jn~TjUdkd53Sw1r0lYohiyDO-cDqWQc5ATJeN9k9TlO939pkTOn3aOFWmBp8b9~0QXkMCUEqgP~cdCFUmI4K4dQ6Taixt83W3Bw0cB0nWK9esBugT~J6SXKT66kp9uK-Ajc3i~rBkRE5HMBTV9rlDJHUg0EVw4xCS0madLwQ28ON0mxSMiO~hVvztcAW5a~Mtf~U~4STezGW~AytqbRsq586E4SI19rLzn0cIa4yEh2YpY6YnH3YC7vwma3olw9VUI8oCUkU9frphzwHpR4W5B-DFlprNjVzR7-1cdWN92MCLn0350S8FiKWuYHXy1csag__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydro-d-23-00121gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/2/10.2166_hydro.2024.121/1/m_hydro-d-23-00121gf01.png?Expires=1712248319&Signature=MBiDDIfBANl5gcv~tx56Jn~TjUdkd53Sw1r0lYohiyDO-cDqWQc5ATJeN9k9TlO939pkTOn3aOFWmBp8b9~0QXkMCUEqgP~cdCFUmI4K4dQ6Taixt83W3Bw0cB0nWK9esBugT~J6SXKT66kp9uK-Ajc3i~rBkRE5HMBTV9rlDJHUg0EVw4xCS0madLwQ28ON0mxSMiO~hVvztcAW5a~Mtf~U~4STezGW~AytqbRsq586E4SI19rLzn0cIa4yEh2YpY6YnH3YC7vwma3olw9VUI8oCUkU9frphzwHpR4W5B-DFlprNjVzR7-1cdWN92MCLn0350S8FiKWuYHXy1csag__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>In recent years, line-shaped rainbands (LRBs) have increased in Hokkaido, Japan. LRBs caused several flood disasters historically, thus the weather patterns that cause them need to be investigated. This study aimed to understand statistically the relationship between LRBs and weather patterns during the summer months under climate change conditions. Our study investigates the link between LRBs and weather patterns in Hokkaido during July and August, using historical and climate prediction
{"title":"Meteorological characteristics of line-shaped rainbands in northern Japan and its surrounding seas under climate change","authors":"Yuta Ohya, Tomohito J. Yamada","doi":"10.2166/hydro.2024.121","DOIUrl":"https://doi.org/10.2166/hydro.2024.121","url":null,"abstract":"<div><div data- reveal-group-><div><img alt=\"graphic\" data-src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/2/10.2166_hydro.2024.121/1/m_hydro-d-23-00121gf01.png?Expires=1712248319&Signature=MBiDDIfBANl5gcv~tx56Jn~TjUdkd53Sw1r0lYohiyDO-cDqWQc5ATJeN9k9TlO939pkTOn3aOFWmBp8b9~0QXkMCUEqgP~cdCFUmI4K4dQ6Taixt83W3Bw0cB0nWK9esBugT~J6SXKT66kp9uK-Ajc3i~rBkRE5HMBTV9rlDJHUg0EVw4xCS0madLwQ28ON0mxSMiO~hVvztcAW5a~Mtf~U~4STezGW~AytqbRsq586E4SI19rLzn0cIa4yEh2YpY6YnH3YC7vwma3olw9VUI8oCUkU9frphzwHpR4W5B-DFlprNjVzR7-1cdWN92MCLn0350S8FiKWuYHXy1csag__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydro-d-23-00121gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/2/10.2166_hydro.2024.121/1/m_hydro-d-23-00121gf01.png?Expires=1712248319&Signature=MBiDDIfBANl5gcv~tx56Jn~TjUdkd53Sw1r0lYohiyDO-cDqWQc5ATJeN9k9TlO939pkTOn3aOFWmBp8b9~0QXkMCUEqgP~cdCFUmI4K4dQ6Taixt83W3Bw0cB0nWK9esBugT~J6SXKT66kp9uK-Ajc3i~rBkRE5HMBTV9rlDJHUg0EVw4xCS0madLwQ28ON0mxSMiO~hVvztcAW5a~Mtf~U~4STezGW~AytqbRsq586E4SI19rLzn0cIa4yEh2YpY6YnH3YC7vwma3olw9VUI8oCUkU9frphzwHpR4W5B-DFlprNjVzR7-1cdWN92MCLn0350S8FiKWuYHXy1csag__&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/26/2/10.2166_hydro.2024.121/1/m_hydro-d-23-00121gf01.png?Expires=1712248319&Signature=MBiDDIfBANl5gcv~tx56Jn~TjUdkd53Sw1r0lYohiyDO-cDqWQc5ATJeN9k9TlO939pkTOn3aOFWmBp8b9~0QXkMCUEqgP~cdCFUmI4K4dQ6Taixt83W3Bw0cB0nWK9esBugT~J6SXKT66kp9uK-Ajc3i~rBkRE5HMBTV9rlDJHUg0EVw4xCS0madLwQ28ON0mxSMiO~hVvztcAW5a~Mtf~U~4STezGW~AytqbRsq586E4SI19rLzn0cIa4yEh2YpY6YnH3YC7vwma3olw9VUI8oCUkU9frphzwHpR4W5B-DFlprNjVzR7-1cdWN92MCLn0350S8FiKWuYHXy1csag__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydro-d-23-00121gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/2/10.2166_hydro.2024.121/1/m_hydro-d-23-00121gf01.png?Expires=1712248319&Signature=MBiDDIfBANl5gcv~tx56Jn~TjUdkd53Sw1r0lYohiyDO-cDqWQc5ATJeN9k9TlO939pkTOn3aOFWmBp8b9~0QXkMCUEqgP~cdCFUmI4K4dQ6Taixt83W3Bw0cB0nWK9esBugT~J6SXKT66kp9uK-Ajc3i~rBkRE5HMBTV9rlDJHUg0EVw4xCS0madLwQ28ON0mxSMiO~hVvztcAW5a~Mtf~U~4STezGW~AytqbRsq586E4SI19rLzn0cIa4yEh2YpY6YnH3YC7vwma3olw9VUI8oCUkU9frphzwHpR4W5B-DFlprNjVzR7-1cdWN92MCLn0350S8FiKWuYHXy1csag__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>In recent years, line-shaped rainbands (LRBs) have increased in Hokkaido, Japan. LRBs caused several flood disasters historically, thus the weather patterns that cause them need to be investigated. This study aimed to understand statistically the relationship between LRBs and weather patterns during the summer months under climate change conditions. Our study investigates the link between LRBs and weather patterns in Hokkaido during July and August, using historical and climate prediction","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"33 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140004998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<div><div data- reveal-group-><div><img alt="graphic" data-src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/2/10.2166_hydro.2024.154/1/m_hydro-d-23-00154gf01.png?Expires=1712246905&Signature=l7k9ixdsA6TvOf4cuuVzLNAo8suokFyYQaEjqpHVCPG66-4u~GJsd5D4TZDRd0rVz70ykR0UyLf34NDPsGd8qQ6jNW0bhGPpqGTz2SME1Apw23RLHbpdLJkNXCgufLrbQJOXg-pXfq4Uo0pYjsVYH8M8OtuFjgGLXju0BKnLSjUBo1qCz~nYYD6dhv~eiGcB1R5Y5x9yeRAj02lHfhNH7RDgJPultNx1QFQd3FWSH1vp0eSFYixbu6Mirm5yi94MwYkrf9gS3MnJq-1zIS8HKGlLm6CzoUVr4t2JFbXEd4dKbkus8NiwQkzbdaF-r8o63eCFH9BBtKSgEmXkwj4Sfw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydro-d-23-00154gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/2/10.2166_hydro.2024.154/1/m_hydro-d-23-00154gf01.png?Expires=1712246905&Signature=l7k9ixdsA6TvOf4cuuVzLNAo8suokFyYQaEjqpHVCPG66-4u~GJsd5D4TZDRd0rVz70ykR0UyLf34NDPsGd8qQ6jNW0bhGPpqGTz2SME1Apw23RLHbpdLJkNXCgufLrbQJOXg-pXfq4Uo0pYjsVYH8M8OtuFjgGLXju0BKnLSjUBo1qCz~nYYD6dhv~eiGcB1R5Y5x9yeRAj02lHfhNH7RDgJPultNx1QFQd3FWSH1vp0eSFYixbu6Mirm5yi94MwYkrf9gS3MnJq-1zIS8HKGlLm6CzoUVr4t2JFbXEd4dKbkus8NiwQkzbdaF-r8o63eCFH9BBtKSgEmXkwj4Sfw__&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/26/2/10.2166_hydro.2024.154/1/m_hydro-d-23-00154gf01.png?Expires=1712246905&Signature=l7k9ixdsA6TvOf4cuuVzLNAo8suokFyYQaEjqpHVCPG66-4u~GJsd5D4TZDRd0rVz70ykR0UyLf34NDPsGd8qQ6jNW0bhGPpqGTz2SME1Apw23RLHbpdLJkNXCgufLrbQJOXg-pXfq4Uo0pYjsVYH8M8OtuFjgGLXju0BKnLSjUBo1qCz~nYYD6dhv~eiGcB1R5Y5x9yeRAj02lHfhNH7RDgJPultNx1QFQd3FWSH1vp0eSFYixbu6Mirm5yi94MwYkrf9gS3MnJq-1zIS8HKGlLm6CzoUVr4t2JFbXEd4dKbkus8NiwQkzbdaF-r8o63eCFH9BBtKSgEmXkwj4Sfw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydro-d-23-00154gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/2/10.2166_hydro.2024.154/1/m_hydro-d-23-00154gf01.png?Expires=1712246905&Signature=l7k9ixdsA6TvOf4cuuVzLNAo8suokFyYQaEjqpHVCPG66-4u~GJsd5D4TZDRd0rVz70ykR0UyLf34NDPsGd8qQ6jNW0bhGPpqGTz2SME1Apw23RLHbpdLJkNXCgufLrbQJOXg-pXfq4Uo0pYjsVYH8M8OtuFjgGLXju0BKnLSjUBo1qCz~nYYD6dhv~eiGcB1R5Y5x9yeRAj02lHfhNH7RDgJPultNx1QFQd3FWSH1vp0eSFYixbu6Mirm5yi94MwYkrf9gS3MnJq-1zIS8HKGlLm6CzoUVr4t2JFbXEd4dKbkus8NiwQkzbdaF-r8o63eCFH9BBtKSgEmXkwj4Sfw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>Effective management of water resources is heavily dependent on accurate knowledge of rainfall patterns. Satellite rainfall estimates (SREs) have become increasingly popular due to their ability to provide spatial rainfall data. However, the accuracy of SREs is limited by a variety of factors including a lack of observations, inadequate evaluation techniques, and the use of short evaluation durations. To improve our understanding of SREs, this study evaluated the long-term performance of
{"title":"Evaluation of satellite rainfall estimates using PERSIANN-CDR and TRMM over three critical cells in Jordan","authors":"Mohanned Al-Sheriadeh, Anas Riyad Al-Sharman","doi":"10.2166/hydro.2024.154","DOIUrl":"https://doi.org/10.2166/hydro.2024.154","url":null,"abstract":"<div><div data- reveal-group-><div><img alt=\"graphic\" data-src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/2/10.2166_hydro.2024.154/1/m_hydro-d-23-00154gf01.png?Expires=1712246905&Signature=l7k9ixdsA6TvOf4cuuVzLNAo8suokFyYQaEjqpHVCPG66-4u~GJsd5D4TZDRd0rVz70ykR0UyLf34NDPsGd8qQ6jNW0bhGPpqGTz2SME1Apw23RLHbpdLJkNXCgufLrbQJOXg-pXfq4Uo0pYjsVYH8M8OtuFjgGLXju0BKnLSjUBo1qCz~nYYD6dhv~eiGcB1R5Y5x9yeRAj02lHfhNH7RDgJPultNx1QFQd3FWSH1vp0eSFYixbu6Mirm5yi94MwYkrf9gS3MnJq-1zIS8HKGlLm6CzoUVr4t2JFbXEd4dKbkus8NiwQkzbdaF-r8o63eCFH9BBtKSgEmXkwj4Sfw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydro-d-23-00154gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/2/10.2166_hydro.2024.154/1/m_hydro-d-23-00154gf01.png?Expires=1712246905&Signature=l7k9ixdsA6TvOf4cuuVzLNAo8suokFyYQaEjqpHVCPG66-4u~GJsd5D4TZDRd0rVz70ykR0UyLf34NDPsGd8qQ6jNW0bhGPpqGTz2SME1Apw23RLHbpdLJkNXCgufLrbQJOXg-pXfq4Uo0pYjsVYH8M8OtuFjgGLXju0BKnLSjUBo1qCz~nYYD6dhv~eiGcB1R5Y5x9yeRAj02lHfhNH7RDgJPultNx1QFQd3FWSH1vp0eSFYixbu6Mirm5yi94MwYkrf9gS3MnJq-1zIS8HKGlLm6CzoUVr4t2JFbXEd4dKbkus8NiwQkzbdaF-r8o63eCFH9BBtKSgEmXkwj4Sfw__&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/26/2/10.2166_hydro.2024.154/1/m_hydro-d-23-00154gf01.png?Expires=1712246905&Signature=l7k9ixdsA6TvOf4cuuVzLNAo8suokFyYQaEjqpHVCPG66-4u~GJsd5D4TZDRd0rVz70ykR0UyLf34NDPsGd8qQ6jNW0bhGPpqGTz2SME1Apw23RLHbpdLJkNXCgufLrbQJOXg-pXfq4Uo0pYjsVYH8M8OtuFjgGLXju0BKnLSjUBo1qCz~nYYD6dhv~eiGcB1R5Y5x9yeRAj02lHfhNH7RDgJPultNx1QFQd3FWSH1vp0eSFYixbu6Mirm5yi94MwYkrf9gS3MnJq-1zIS8HKGlLm6CzoUVr4t2JFbXEd4dKbkus8NiwQkzbdaF-r8o63eCFH9BBtKSgEmXkwj4Sfw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydro-d-23-00154gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/2/10.2166_hydro.2024.154/1/m_hydro-d-23-00154gf01.png?Expires=1712246905&Signature=l7k9ixdsA6TvOf4cuuVzLNAo8suokFyYQaEjqpHVCPG66-4u~GJsd5D4TZDRd0rVz70ykR0UyLf34NDPsGd8qQ6jNW0bhGPpqGTz2SME1Apw23RLHbpdLJkNXCgufLrbQJOXg-pXfq4Uo0pYjsVYH8M8OtuFjgGLXju0BKnLSjUBo1qCz~nYYD6dhv~eiGcB1R5Y5x9yeRAj02lHfhNH7RDgJPultNx1QFQd3FWSH1vp0eSFYixbu6Mirm5yi94MwYkrf9gS3MnJq-1zIS8HKGlLm6CzoUVr4t2JFbXEd4dKbkus8NiwQkzbdaF-r8o63eCFH9BBtKSgEmXkwj4Sfw__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>Effective management of water resources is heavily dependent on accurate knowledge of rainfall patterns. Satellite rainfall estimates (SREs) have become increasingly popular due to their ability to provide spatial rainfall data. However, the accuracy of SREs is limited by a variety of factors including a lack of observations, inadequate evaluation techniques, and the use of short evaluation durations. To improve our understanding of SREs, this study evaluated the long-term performance of ","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"10 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140005485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zeda Yin, Yasaman Saadati, Beichao Hu, Arturo S. Leon, M. H. Amini, Dwayne McDaniel
Flooding is one of the most frequent natural hazards and causes more economic loss than all the other natural hazards. Fast and accurate flood prediction has significance in preserving lives, minimizing economic damage, and reducing public health risks. However, current methods cannot achieve speed and accuracy simultaneously. Numerical methods can provide high-fidelity results, but they are time-consuming, particularly when pursuing high accuracy. Conversely, neural networks can provide results in a matter of seconds, but they have shown low accuracy in flood map generation by all existing methods. This work combines the strengths of numerical methods and neural networks and builds a framework that can quickly and accurately model the high-fidelity flood inundation map with detailed water depth information. In this paper, we employ the U-Net and generative adversarial network (GAN) models to recover the lost physics and information from ultra-fast, low-resolution numerical simulations, ultimately presenting high-resolution, high-fidelity flood maps as the end results. In this study, both the U-Net and GAN models have proven their ability to reduce the computation time for generating high-fidelity results, reducing it from 7–8 h down to 1 min. Furthermore, the accuracy of both models is notably high.
{"title":"Fast high-fidelity flood inundation map generation by super-resolution techniques","authors":"Zeda Yin, Yasaman Saadati, Beichao Hu, Arturo S. Leon, M. H. Amini, Dwayne McDaniel","doi":"10.2166/hydro.2024.228","DOIUrl":"https://doi.org/10.2166/hydro.2024.228","url":null,"abstract":"\u0000 \u0000 Flooding is one of the most frequent natural hazards and causes more economic loss than all the other natural hazards. Fast and accurate flood prediction has significance in preserving lives, minimizing economic damage, and reducing public health risks. However, current methods cannot achieve speed and accuracy simultaneously. Numerical methods can provide high-fidelity results, but they are time-consuming, particularly when pursuing high accuracy. Conversely, neural networks can provide results in a matter of seconds, but they have shown low accuracy in flood map generation by all existing methods. This work combines the strengths of numerical methods and neural networks and builds a framework that can quickly and accurately model the high-fidelity flood inundation map with detailed water depth information. In this paper, we employ the U-Net and generative adversarial network (GAN) models to recover the lost physics and information from ultra-fast, low-resolution numerical simulations, ultimately presenting high-resolution, high-fidelity flood maps as the end results. In this study, both the U-Net and GAN models have proven their ability to reduce the computation time for generating high-fidelity results, reducing it from 7–8 h down to 1 min. Furthermore, the accuracy of both models is notably high.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"31 9","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139389778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In situ groundwater remediation technique is a commonly adopted method for the treatment of contaminated groundwater and the porous media associated with it. Engineered Injection and Extraction (EIE) has evolved as an improved methodology for in situ remediation, where sequential injection and extraction of clean water around the treatment area enhances the spreading of treatment reagents by inducing additional flow fields. Conventional EIE studies were based on flow fields in two dimensions. There are only limited experimental and theoretical studies exploring the potential of inducing a three-dimensional flow field using EIE. The present study experimentally and numerically evaluates the effect of a three-dimensional flow field induced by partially screened wells. EIE experiments were conducted on a laboratory-scale aquifer model with laterite soil as the porous medium. Tracer transport in porous medium was studied by measuring the concentration at various observation points and enhanced dilution was observed when EIE was employed with partially screened wells. Experimental observations were also used to calibrate and validate the numerical model developed using Visual MODFLOW Flex. Enhancement in spreading was quantified in terms of concentration mass attenuation and maximum mass attenuation was observed when EIE was employed with partially screened wells.
地下水原位修复技术是处理受污染地下水及其相关多孔介质的常用方法。工程注入和抽取（EIE）是一种经过改进的原位修复方法，通过在处理区域周围连续注入和抽取清洁水，诱导额外的流场，从而加强处理试剂的扩散。传统的 EIE 研究基于二维流场。只有有限的实验和理论研究探讨了利用 EIE 诱导三维流场的潜力。本研究通过实验和数值方法评估了部分屏蔽井诱导三维流场的效果。EIE 实验是在以红土为多孔介质的实验室规模含水层模型上进行的。通过测量不同观测点的浓度，研究了示踪剂在多孔介质中的迁移情况。实验观测结果还用于校准和验证使用 Visual MODFLOW Flex 开发的数值模型。以浓度质量衰减的方式对扩散的增强进行了量化，当 EIE 与部分屏蔽井一起使用时，观察到了最大的质量衰减。
{"title":"Experimental and numerical investigation of Engineered Injection and Extraction (EIE) induced with three-dimensional flow field","authors":"Farsana M. Asha, N. Sajikumar, E. A. Subaida","doi":"10.2166/hydro.2023.427","DOIUrl":"https://doi.org/10.2166/hydro.2023.427","url":null,"abstract":"<p>In situ groundwater remediation technique is a commonly adopted method for the treatment of contaminated groundwater and the porous media associated with it. Engineered Injection and Extraction (EIE) has evolved as an improved methodology for in situ remediation, where sequential injection and extraction of clean water around the treatment area enhances the spreading of treatment reagents by inducing additional flow fields. Conventional EIE studies were based on flow fields in two dimensions. There are only limited experimental and theoretical studies exploring the potential of inducing a three-dimensional flow field using EIE. The present study experimentally and numerically evaluates the effect of a three-dimensional flow field induced by partially screened wells. EIE experiments were conducted on a laboratory-scale aquifer model with laterite soil as the porous medium. Tracer transport in porous medium was studied by measuring the concentration at various observation points and enhanced dilution was observed when EIE was employed with partially screened wells. Experimental observations were also used to calibrate and validate the numerical model developed using Visual MODFLOW Flex. Enhancement in spreading was quantified in terms of concentration mass attenuation and maximum mass attenuation was observed when EIE was employed with partially screened wells.</p>","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"56 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139667758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<div><div data- reveal-group-><div><img alt="graphic" data-src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/1/10.2166_hydro.2023.182/1/m_hydro-d-23-00182gf01.png?Expires=1709841441&Signature=xMpA~wkS~~ifqqTv54V4piyxjUgUzcs~7OX5BsDb89KGB7W7tL4wLUV0fn5ZBvX6rHt0FyROAQwVfwYKc-vwiqnvNVEZybJDkM47NiOZWr2F7tRZUUq7AjEsvjPnQG-sEK57ediQeASVW3dalVMRUYV-2e2mOzBL3kteDIRy9tHCrZdWUyuzn2zyiDr8DgHWpbZA7EfsE9FDq1OlxKzYs4Ugvf2R9mFw-iNCyeDmJEkUz0Y8CZaqDKWQcKurJiQg1A23ZNglRXdo5mt1e8dYiW~zUDiIENBVf1sJujF6XCitsB33fhZmUgJHwuBhUkRBHiuQOG0LyXac89hEs~f~aA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydro-d-23-00182gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/1/10.2166_hydro.2023.182/1/m_hydro-d-23-00182gf01.png?Expires=1709841441&Signature=xMpA~wkS~~ifqqTv54V4piyxjUgUzcs~7OX5BsDb89KGB7W7tL4wLUV0fn5ZBvX6rHt0FyROAQwVfwYKc-vwiqnvNVEZybJDkM47NiOZWr2F7tRZUUq7AjEsvjPnQG-sEK57ediQeASVW3dalVMRUYV-2e2mOzBL3kteDIRy9tHCrZdWUyuzn2zyiDr8DgHWpbZA7EfsE9FDq1OlxKzYs4Ugvf2R9mFw-iNCyeDmJEkUz0Y8CZaqDKWQcKurJiQg1A23ZNglRXdo5mt1e8dYiW~zUDiIENBVf1sJujF6XCitsB33fhZmUgJHwuBhUkRBHiuQOG0LyXac89hEs~f~aA__&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/26/1/10.2166_hydro.2023.182/1/m_hydro-d-23-00182gf01.png?Expires=1709841441&Signature=xMpA~wkS~~ifqqTv54V4piyxjUgUzcs~7OX5BsDb89KGB7W7tL4wLUV0fn5ZBvX6rHt0FyROAQwVfwYKc-vwiqnvNVEZybJDkM47NiOZWr2F7tRZUUq7AjEsvjPnQG-sEK57ediQeASVW3dalVMRUYV-2e2mOzBL3kteDIRy9tHCrZdWUyuzn2zyiDr8DgHWpbZA7EfsE9FDq1OlxKzYs4Ugvf2R9mFw-iNCyeDmJEkUz0Y8CZaqDKWQcKurJiQg1A23ZNglRXdo5mt1e8dYiW~zUDiIENBVf1sJujF6XCitsB33fhZmUgJHwuBhUkRBHiuQOG0LyXac89hEs~f~aA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" path-from-xml="hydro-d-23-00182gf01.tif" src="https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/1/10.2166_hydro.2023.182/1/m_hydro-d-23-00182gf01.png?Expires=1709841441&Signature=xMpA~wkS~~ifqqTv54V4piyxjUgUzcs~7OX5BsDb89KGB7W7tL4wLUV0fn5ZBvX6rHt0FyROAQwVfwYKc-vwiqnvNVEZybJDkM47NiOZWr2F7tRZUUq7AjEsvjPnQG-sEK57ediQeASVW3dalVMRUYV-2e2mOzBL3kteDIRy9tHCrZdWUyuzn2zyiDr8DgHWpbZA7EfsE9FDq1OlxKzYs4Ugvf2R9mFw-iNCyeDmJEkUz0Y8CZaqDKWQcKurJiQg1A23ZNglRXdo5mt1e8dYiW~zUDiIENBVf1sJujF6XCitsB33fhZmUgJHwuBhUkRBHiuQOG0LyXac89hEs~f~aA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>The Soil and Water Assessment Tool (SWAT) has been widely applied to simulate the hydrological cycle, investigate cause-and-effect relationships, and aid decision-making for better watershed management. However, the software tools for model dataset analysis and visualization to support informed decision-making in a web environment are not considered fully fledged and are technically intensive to implement. This study focuses on addressing these issues by establishing a tool and library (n
{"title":"PAVLIB4SWAT: a Python analysis and visualization tool and library based on Kepler.gl for SWAT models","authors":"Qiaoying Lin, Dejian Zhang, Jiefeng Wu, Yihui Fang, Xingwei Chen, Bingqing Lin","doi":"10.2166/hydro.2023.182","DOIUrl":"https://doi.org/10.2166/hydro.2023.182","url":null,"abstract":"<div><div data- reveal-group-><div><img alt=\"graphic\" data-src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/1/10.2166_hydro.2023.182/1/m_hydro-d-23-00182gf01.png?Expires=1709841441&Signature=xMpA~wkS~~ifqqTv54V4piyxjUgUzcs~7OX5BsDb89KGB7W7tL4wLUV0fn5ZBvX6rHt0FyROAQwVfwYKc-vwiqnvNVEZybJDkM47NiOZWr2F7tRZUUq7AjEsvjPnQG-sEK57ediQeASVW3dalVMRUYV-2e2mOzBL3kteDIRy9tHCrZdWUyuzn2zyiDr8DgHWpbZA7EfsE9FDq1OlxKzYs4Ugvf2R9mFw-iNCyeDmJEkUz0Y8CZaqDKWQcKurJiQg1A23ZNglRXdo5mt1e8dYiW~zUDiIENBVf1sJujF6XCitsB33fhZmUgJHwuBhUkRBHiuQOG0LyXac89hEs~f~aA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydro-d-23-00182gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/1/10.2166_hydro.2023.182/1/m_hydro-d-23-00182gf01.png?Expires=1709841441&Signature=xMpA~wkS~~ifqqTv54V4piyxjUgUzcs~7OX5BsDb89KGB7W7tL4wLUV0fn5ZBvX6rHt0FyROAQwVfwYKc-vwiqnvNVEZybJDkM47NiOZWr2F7tRZUUq7AjEsvjPnQG-sEK57ediQeASVW3dalVMRUYV-2e2mOzBL3kteDIRy9tHCrZdWUyuzn2zyiDr8DgHWpbZA7EfsE9FDq1OlxKzYs4Ugvf2R9mFw-iNCyeDmJEkUz0Y8CZaqDKWQcKurJiQg1A23ZNglRXdo5mt1e8dYiW~zUDiIENBVf1sJujF6XCitsB33fhZmUgJHwuBhUkRBHiuQOG0LyXac89hEs~f~aA__&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/26/1/10.2166_hydro.2023.182/1/m_hydro-d-23-00182gf01.png?Expires=1709841441&Signature=xMpA~wkS~~ifqqTv54V4piyxjUgUzcs~7OX5BsDb89KGB7W7tL4wLUV0fn5ZBvX6rHt0FyROAQwVfwYKc-vwiqnvNVEZybJDkM47NiOZWr2F7tRZUUq7AjEsvjPnQG-sEK57ediQeASVW3dalVMRUYV-2e2mOzBL3kteDIRy9tHCrZdWUyuzn2zyiDr8DgHWpbZA7EfsE9FDq1OlxKzYs4Ugvf2R9mFw-iNCyeDmJEkUz0Y8CZaqDKWQcKurJiQg1A23ZNglRXdo5mt1e8dYiW~zUDiIENBVf1sJujF6XCitsB33fhZmUgJHwuBhUkRBHiuQOG0LyXac89hEs~f~aA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\" path-from-xml=\"hydro-d-23-00182gf01.tif\" src=\"https://iwa.silverchair-cdn.com/iwa/content_public/journal/jh/26/1/10.2166_hydro.2023.182/1/m_hydro-d-23-00182gf01.png?Expires=1709841441&Signature=xMpA~wkS~~ifqqTv54V4piyxjUgUzcs~7OX5BsDb89KGB7W7tL4wLUV0fn5ZBvX6rHt0FyROAQwVfwYKc-vwiqnvNVEZybJDkM47NiOZWr2F7tRZUUq7AjEsvjPnQG-sEK57ediQeASVW3dalVMRUYV-2e2mOzBL3kteDIRy9tHCrZdWUyuzn2zyiDr8DgHWpbZA7EfsE9FDq1OlxKzYs4Ugvf2R9mFw-iNCyeDmJEkUz0Y8CZaqDKWQcKurJiQg1A23ZNglRXdo5mt1e8dYiW~zUDiIENBVf1sJujF6XCitsB33fhZmUgJHwuBhUkRBHiuQOG0LyXac89hEs~f~aA__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA\"/><div>View largeDownload slide</div></div><i> </i><span>Close modal</span></div></div><p>The Soil and Water Assessment Tool (SWAT) has been widely applied to simulate the hydrological cycle, investigate cause-and-effect relationships, and aid decision-making for better watershed management. However, the software tools for model dataset analysis and visualization to support informed decision-making in a web environment are not considered fully fledged and are technically intensive to implement. This study focuses on addressing these issues by establishing a tool and library (n","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"56 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139667466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, the discharge coefficient prediction model for this structure in a subcritical flow regime is first established by extreme learning machine (ELM) and Bayesian network, and the model's performance is analyzed and verified in detail. In addition, the global sensitivity analysis method is introduced to the optimal prediction model to analyze the sensitivity for the dimensionless parameters affecting the discharge coefficient. The results show that the Bayesian extreme learning machine (BELM) can effectively predict the discharge coefficients of the symmetric stepped labyrinth side weir. The range of 95% confidence interval [−0.055,0.040] is also significantly smaller than that of the ELM ([−0.089,0.076]) and the Kernel extreme learning machine (KELM) ([−0.091,0.081]) at the testing stage. The dimensionless parameter ratio of upstream water depth of stepped labyrinth side weir p/y1 has the greatest effect on the discharge coefficient Cd, accounting for 55.57 and 54.17% under single action and other parameter interactions, respectively. Dimensionless step number bs/L has little effect on Cd, which can be ignored. Meanwhile, when the number of steps is less (N = 4) and the internal head angle is smaller (θ = 45°), a larger discharge coefficient value can be obtained.
{"title":"Analysis of discharge characteristics of a symmetrical stepped labyrinth side weir based on global sensitivity","authors":"Wuyi Wan, Guiying Shen, Shanshan Li, Abbas Parsaie, Yuhang Wang, Yu Zhou","doi":"10.2166/hydro.2023.260","DOIUrl":"https://doi.org/10.2166/hydro.2023.260","url":null,"abstract":"\u0000 In this paper, the discharge coefficient prediction model for this structure in a subcritical flow regime is first established by extreme learning machine (ELM) and Bayesian network, and the model's performance is analyzed and verified in detail. In addition, the global sensitivity analysis method is introduced to the optimal prediction model to analyze the sensitivity for the dimensionless parameters affecting the discharge coefficient. The results show that the Bayesian extreme learning machine (BELM) can effectively predict the discharge coefficients of the symmetric stepped labyrinth side weir. The range of 95% confidence interval [−0.055,0.040] is also significantly smaller than that of the ELM ([−0.089,0.076]) and the Kernel extreme learning machine (KELM) ([−0.091,0.081]) at the testing stage. The dimensionless parameter ratio of upstream water depth of stepped labyrinth side weir p/y1 has the greatest effect on the discharge coefficient Cd, accounting for 55.57 and 54.17% under single action and other parameter interactions, respectively. Dimensionless step number bs/L has little effect on Cd, which can be ignored. Meanwhile, when the number of steps is less (N = 4) and the internal head angle is smaller (θ = 45°), a larger discharge coefficient value can be obtained.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"33 5","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138951682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. Zahidi, Mun Ee Yau, Alex Lechner, Karen Lourdes
Social values of land use are often excluded when undertaking integrated flood management as they are harder to quantify. To fill this research gap, a geographic information system application called Social Values for Ecosystem Services was used to assess, map and quantify the perceived social values of flood-prone land use in Kuala Selangor, Malaysia. This approach was based on a non-monetary value index (VI) calculated from responses to a quantitative social survey on the public's attitude and preference towards flood management across different land uses. The study outcome is the geospatial representation of flood-prone land use with their social values, which local communities perceive as crucial for flood management. The VI was influenced by elevation and slope, with lower elevations and flatter slopes associated with higher values. Farmland is highly favoured by the local community for flood management, whereas oil palm and rubber plantations are opposed. Tourism received the highest monetary allocations from survey respondents, with the popular firefly park consistently associated with the highest social values. This practical framework contributes to integrated flood management in facilitating decision-makers to evaluate land-use trade-offs by considering their social values when prioritising flood mitigation measures or investments.
{"title":"Modelling public social values of flood-prone land use using the GIS application SolVES","authors":"I. Zahidi, Mun Ee Yau, Alex Lechner, Karen Lourdes","doi":"10.2166/hydro.2023.010","DOIUrl":"https://doi.org/10.2166/hydro.2023.010","url":null,"abstract":"\u0000 Social values of land use are often excluded when undertaking integrated flood management as they are harder to quantify. To fill this research gap, a geographic information system application called Social Values for Ecosystem Services was used to assess, map and quantify the perceived social values of flood-prone land use in Kuala Selangor, Malaysia. This approach was based on a non-monetary value index (VI) calculated from responses to a quantitative social survey on the public's attitude and preference towards flood management across different land uses. The study outcome is the geospatial representation of flood-prone land use with their social values, which local communities perceive as crucial for flood management. The VI was influenced by elevation and slope, with lower elevations and flatter slopes associated with higher values. Farmland is highly favoured by the local community for flood management, whereas oil palm and rubber plantations are opposed. Tourism received the highest monetary allocations from survey respondents, with the popular firefly park consistently associated with the highest social values. This practical framework contributes to integrated flood management in facilitating decision-makers to evaluate land-use trade-offs by considering their social values when prioritising flood mitigation measures or investments.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"7 7","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138968040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chien Quyet Nguyen, Tuyen Thi Tran, Trang Thanh Thi Nguyen, Thuy Ha Thi Nguyen, T. S. Astarkhanova, Luong Van Vu, Khac Tai Dau, Hieu Ngoc Nguyen, Giang Hương Pham, D. Nguyen, Indra Prakash, Binh Pham
Soil Erosion Susceptibility Mapping (SESM) is one of the practical approaches for managing and mitigating soil erosion. This study applied four Machine Learning (ML) models namely the Multilayer Perceptron (MLP) classifier, AdaBoost, Ridge classifier, and Gradient Boosting classifier to perform SESM in a region of Nghe An province, Vietnam. The development of these models incorporated seven factors influencing soil erosion: slope degree, slope aspect, curvature, elevation, Normalized Difference Vegetation Index (NDVI), rainfall, and soil type. These factors were determined based on 685 identified soil erosion locations. According to SHapley Additive exPlanations (SHAP) analysis, soil type emerged as the most significant factor influencing soil erosion. Among all the developed models, the Gradient Boosting classifier demonstrated the highest prediction power, followed by the MLP classifier, Ridge classifier, and AdaBoost, respectively. Therefore, the Gradient Boosting classifier is recommended for accurate SESM in other regions too, taking into account the local geo-environmental factors.
{"title":"Mapping of soil erosion susceptibility using advanced machine learning models at Nghe An, Vietnam","authors":"Chien Quyet Nguyen, Tuyen Thi Tran, Trang Thanh Thi Nguyen, Thuy Ha Thi Nguyen, T. S. Astarkhanova, Luong Van Vu, Khac Tai Dau, Hieu Ngoc Nguyen, Giang Hương Pham, D. Nguyen, Indra Prakash, Binh Pham","doi":"10.2166/hydro.2023.327","DOIUrl":"https://doi.org/10.2166/hydro.2023.327","url":null,"abstract":"\u0000 Soil Erosion Susceptibility Mapping (SESM) is one of the practical approaches for managing and mitigating soil erosion. This study applied four Machine Learning (ML) models namely the Multilayer Perceptron (MLP) classifier, AdaBoost, Ridge classifier, and Gradient Boosting classifier to perform SESM in a region of Nghe An province, Vietnam. The development of these models incorporated seven factors influencing soil erosion: slope degree, slope aspect, curvature, elevation, Normalized Difference Vegetation Index (NDVI), rainfall, and soil type. These factors were determined based on 685 identified soil erosion locations. According to SHapley Additive exPlanations (SHAP) analysis, soil type emerged as the most significant factor influencing soil erosion. Among all the developed models, the Gradient Boosting classifier demonstrated the highest prediction power, followed by the MLP classifier, Ridge classifier, and AdaBoost, respectively. Therefore, the Gradient Boosting classifier is recommended for accurate SESM in other regions too, taking into account the local geo-environmental factors.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"57 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139000272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lyce Ndolo Umba, Ilham Yahya Amir, Gebre Gelete, Hüseyin Gökçekuş, Ikenna D. Uwanuakwa
Rainfall-runoff modelling is a critical component of hydrological studies, and its accuracy is essential for water resource management. Recent advances in machine learning have led to the development of more sophisticated rainfall-runoff models, but there is still room for improvement. This study proposes a novel approach to streamflow modelling that uses the artificial hummingbird algorithm (AHA) to optimize the boosted tree algorithm. the AHA-boosted tree algorithm model was compared against two established methods, the support vector machine (SVM) and the Gaussian process regression (GPR), using a variety of statistical and graphical performance measures. The results showed that the AHA-boosted tree algorithm model significantly outperformed the SVM and GPR models, with an R2 of 0.932, RMSE of 5.358 m3/s, MAE of 2.365 m3/s, and MSE of 28.705 m3/s. The SVM model followed while the GPR model had the least accurate performance. However, all models underperformed in capturing the peak flow of the hydrograph. Evaluations using both statistical and graphical performance measures, including time series plots, scatter plots, and Taylor diagrams, were critical in this assessment. The results suggest that the AHA-boosted tree algorithm could potentially be a superior alternative for enhancing the precision of rainfall-runoff modelling, despite certain challenges in predicting peak flow events.
{"title":"Artificial hummingbird algorithm-optimized boosted tree for improved rainfall-runoff modelling","authors":"Lyce Ndolo Umba, Ilham Yahya Amir, Gebre Gelete, Hüseyin Gökçekuş, Ikenna D. Uwanuakwa","doi":"10.2166/hydro.2023.187","DOIUrl":"https://doi.org/10.2166/hydro.2023.187","url":null,"abstract":"\u0000 Rainfall-runoff modelling is a critical component of hydrological studies, and its accuracy is essential for water resource management. Recent advances in machine learning have led to the development of more sophisticated rainfall-runoff models, but there is still room for improvement. This study proposes a novel approach to streamflow modelling that uses the artificial hummingbird algorithm (AHA) to optimize the boosted tree algorithm. the AHA-boosted tree algorithm model was compared against two established methods, the support vector machine (SVM) and the Gaussian process regression (GPR), using a variety of statistical and graphical performance measures. The results showed that the AHA-boosted tree algorithm model significantly outperformed the SVM and GPR models, with an R2 of 0.932, RMSE of 5.358 m3/s, MAE of 2.365 m3/s, and MSE of 28.705 m3/s. The SVM model followed while the GPR model had the least accurate performance. However, all models underperformed in capturing the peak flow of the hydrograph. Evaluations using both statistical and graphical performance measures, including time series plots, scatter plots, and Taylor diagrams, were critical in this assessment. The results suggest that the AHA-boosted tree algorithm could potentially be a superior alternative for enhancing the precision of rainfall-runoff modelling, despite certain challenges in predicting peak flow events.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"29 7","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139006755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pranit Dongare, Kul Vaibhav Sharma, Vijendra Kumar, Aneesh Mathew
Urban settlement depends on water distribution networks for clean and safe drinking water. This research incorporates geographic information systems (GIS), remote sensing (RS), and hydraulic modelling software EPANET to analyze and construct water distribution systems in Bota town, India. Satellite images and hydrological data have been utilized for management of the Bota town's water supply network, sources to cater the demand for urban centres. EPANET simulates hydraulic behaviour in the water distribution system under different operating situations. EPANET simulation shows network leaks, low pressure, and substantial head loss. These findings have advised for water distribution system improvements by analyzing network shortcomings. Booster pumps, new pipelines, and repairing of existing leakages are examples of such improvements. GIS, remote sensing, and EPANET provided a comprehensive water distribution system study and more accurate and efficient improvement identification. This study emphasizes the necessity of new technologies in water distribution system analysis and design. The study solves Bota town's water distribution system problems of low pressure, high head loss, and leaks utilizing GIS, remote sensing, and EPANET. The findings of this research can help in enhancing the water delivery systems in other towns with comparable issues.
{"title":"Water distribution system modelling of GIS–remote sensing and EPANET for the integrated efficient design","authors":"Pranit Dongare, Kul Vaibhav Sharma, Vijendra Kumar, Aneesh Mathew","doi":"10.2166/hydro.2023.281","DOIUrl":"https://doi.org/10.2166/hydro.2023.281","url":null,"abstract":"\u0000 \u0000 Urban settlement depends on water distribution networks for clean and safe drinking water. This research incorporates geographic information systems (GIS), remote sensing (RS), and hydraulic modelling software EPANET to analyze and construct water distribution systems in Bota town, India. Satellite images and hydrological data have been utilized for management of the Bota town's water supply network, sources to cater the demand for urban centres. EPANET simulates hydraulic behaviour in the water distribution system under different operating situations. EPANET simulation shows network leaks, low pressure, and substantial head loss. These findings have advised for water distribution system improvements by analyzing network shortcomings. Booster pumps, new pipelines, and repairing of existing leakages are examples of such improvements. GIS, remote sensing, and EPANET provided a comprehensive water distribution system study and more accurate and efficient improvement identification. This study emphasizes the necessity of new technologies in water distribution system analysis and design. The study solves Bota town's water distribution system problems of low pressure, high head loss, and leaks utilizing GIS, remote sensing, and EPANET. The findings of this research can help in enhancing the water delivery systems in other towns with comparable issues.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":"28 2","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139004752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：