Determining flood source areas in watersheds using data-driven models and a geographic information system

IF 2.8 3区环境科学与生态学 Q2 WATER RESOURCES

Hydrological Sciences Journal-Journal Des Sciences Hydrologiques Pub Date : 2023-06-28 DOI:10.1080/02626667.2023.2220885

Behzad Zohourian, Seyed Mahmood Hosseini

{"title":"Determining flood source areas in watersheds using data-driven models and a geographic information system","authors":"Behzad Zohourian, Seyed Mahmood Hosseini","doi":"10.1080/02626667.2023.2220885","DOIUrl":null,"url":null,"abstract":"ABSTRACT This study explores the use of gene expression programming (GEP) and artificial neural networks (ANNs) to estimate flood index values based on the unit flood response (UFR) method in two adjacent watersheds (Ardak and Kardeh) located in northeast Iran. The performances of the studied data-driven models were compared according to certain statistical measures such as Root Mean Square Error (RMSE). The findings indicate that GEP models were more accurate than ANNs (RMSE = 0.0986 vs. 0.1512 for Ardak and RMSE = 0.1024 vs. 0.1112 for Kardeh, respectively). Another advantage of the GEP models was providing an explicit relationship between flood index values and physical attributes. As flood index values derived via the UFR method were close to each other, flood contribution area maps were developed using a geographic information system (GIS) to consider uncertainty. Then, fusion algorithms including ordinary averaging, linear regression, and GEP were applied to develop a flexible regional model.","PeriodicalId":55042,"journal":{"name":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","volume":"68 1","pages":"1443 - 1459"},"PeriodicalIF":2.8000,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Hydrological Sciences Journal-Journal Des Sciences Hydrologiques","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1080/02626667.2023.2220885","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"WATER RESOURCES","Score":null,"Total":0}

引用次数: 0

Abstract

ABSTRACT This study explores the use of gene expression programming (GEP) and artificial neural networks (ANNs) to estimate flood index values based on the unit flood response (UFR) method in two adjacent watersheds (Ardak and Kardeh) located in northeast Iran. The performances of the studied data-driven models were compared according to certain statistical measures such as Root Mean Square Error (RMSE). The findings indicate that GEP models were more accurate than ANNs (RMSE = 0.0986 vs. 0.1512 for Ardak and RMSE = 0.1024 vs. 0.1112 for Kardeh, respectively). Another advantage of the GEP models was providing an explicit relationship between flood index values and physical attributes. As flood index values derived via the UFR method were close to each other, flood contribution area maps were developed using a geographic information system (GIS) to consider uncertainty. Then, fusion algorithms including ordinary averaging, linear regression, and GEP were applied to develop a flexible regional model.

查看原文本刊更多论文

利用数据驱动模型和地理信息系统确定流域的洪水源区

摘要:本研究探讨了利用基因表达编程(GEP)和人工神经网络(ann)基于单位洪水响应(UFR)方法估算伊朗东北部两个相邻流域(Ardak和Kardeh)的洪水指数值。根据一定的统计指标，如均方根误差(RMSE)，比较了所研究的数据驱动模型的性能。结果表明，GEP模型比人工神经网络更准确(Ardak的RMSE = 0.0986 vs. 0.1512, Kardeh的RMSE = 0.1024 vs. 0.1112)。GEP模型的另一个优点是提供了洪水指数值与物理属性之间的明确关系。由于通过UFR方法得到的洪水指数彼此接近，因此使用地理信息系统(GIS)开发洪水贡献区图，以考虑不确定性。然后，应用普通平均、线性回归和GEP等融合算法建立柔性区域模型。

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

Hydrological Sciences Journal-Journal Des Sciences Hydrologiques 环境科学-水资源

CiteScore

6.60

自引率

11.40%

发文量

144

审稿时长

9.8 months

期刊介绍： Hydrological Sciences Journal is an international journal focused on hydrology and the relationship of water to atmospheric processes and climate. Hydrological Sciences Journal is the official journal of the International Association of Hydrological Sciences (IAHS). Hydrological Sciences Journal aims to provide a forum for original papers and for the exchange of information and views on significant developments in hydrology worldwide on subjects including: Hydrological cycle and processes Surface water Groundwater Water resource systems and management Geographical factors Earth and atmospheric processes Hydrological extremes and their impact Hydrological Sciences Journal offers a variety of formats for paper submission, including original articles, scientific notes, discussions, and rapid communications.