A bibliometric analysis of trends in rainfall-runoff modeling techniques for urban flood mitigation (2005–2024)

IF 6 Q1 ENGINEERING, MULTIDISCIPLINARY

Results in Engineering Pub Date : 2025-04-14 DOI:10.1016/j.rineng.2025.104927

Abd Rakhim Nanda , Nurnawaty , Amrullah Mansida , Hartono Bancong

{"title":"A bibliometric analysis of trends in rainfall-runoff modeling techniques for urban flood mitigation (2005–2024)","authors":"Abd Rakhim Nanda , Nurnawaty , Amrullah Mansida , Hartono Bancong","doi":"10.1016/j.rineng.2025.104927","DOIUrl":null,"url":null,"abstract":"<div><div>Urban flooding poses significant challenges globally, driven by climate change and rapid urbanization. This bibliometric study reviewed 618 documents published between 2005 and 2024, focusing on rainfall-runoff modelling for urban flood mitigation. Key findings reveal that China (100 publications), the United States (81), and the United Kingdom (55) dominate research output, with emerging contributions from Southeast Asia and the Middle East. Traditional models such as the Storm Water Management Model (SWMM) and the Hydrologic Modelling System (HEC<img>HMS) remain widely used, while machine learning (ML), Geographic Information Systems (GIS), and Low-Impact Development (LID) approaches drive innovation in model precision and adaptability. However, gaps persist in evaluating long-term LID effectiveness and incorporating real-time data to address extreme climate variability. By offering quantitative insights into current research efforts, this analysis highlights the critical need for integrating advanced technologies and sustainable strategies to further enhance resilience in urban flood management frameworks.</div></div>","PeriodicalId":36919,"journal":{"name":"Results in Engineering","volume":"26 ","pages":"Article 104927"},"PeriodicalIF":6.0000,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Results in Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590123025010035","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

Urban flooding poses significant challenges globally, driven by climate change and rapid urbanization. This bibliometric study reviewed 618 documents published between 2005 and 2024, focusing on rainfall-runoff modelling for urban flood mitigation. Key findings reveal that China (100 publications), the United States (81), and the United Kingdom (55) dominate research output, with emerging contributions from Southeast Asia and the Middle East. Traditional models such as the Storm Water Management Model (SWMM) and the Hydrologic Modelling System (HECHMS) remain widely used, while machine learning (ML), Geographic Information Systems (GIS), and Low-Impact Development (LID) approaches drive innovation in model precision and adaptability. However, gaps persist in evaluating long-term LID effectiveness and incorporating real-time data to address extreme climate variability. By offering quantitative insights into current research efforts, this analysis highlights the critical need for integrating advanced technologies and sustainable strategies to further enhance resilience in urban flood management frameworks.

查看原文本刊更多论文

2005-2024年城市防洪降雨径流模拟技术趋势的文献计量学分析

在气候变化和快速城市化的推动下，城市洪水给全球带来了重大挑战。这项文献计量学研究回顾了2005年至2024年间发表的618份文件，重点关注城市洪水缓解的降雨径流模型。主要研究结果显示，中国（100篇）、美国（81篇）和英国（55篇）在研究产出中占主导地位，东南亚和中东的贡献也在不断增加。传统的模型，如雨水管理模型（SWMM）和水文建模系统（HECHMS）仍然被广泛使用，而机器学习（ML）、地理信息系统（GIS）和低影响开发（LID）方法推动了模型精度和适应性的创新。然而，在评估长期LID有效性和整合实时数据以应对极端气候变率方面仍然存在差距。通过对当前研究工作的定量分析，本分析强调了整合先进技术和可持续战略以进一步增强城市洪水管理框架弹性的迫切需要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊