Evaluation of Groundwater Potential Zones Using GIS-Based Machine Learning Ensemble Models in the Gidabo Watershed, Ethiopia

IF 4.4 4区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Global Challenges Pub Date : 2024-10-16 DOI:10.1002/gch2.202400137
Mussa Muhaba Mussa, Tarun Kumar Lohani, Abunu Atlabachew Eshete
{"title":"Evaluation of Groundwater Potential Zones Using GIS-Based Machine Learning Ensemble Models in the Gidabo Watershed, Ethiopia","authors":"Mussa Muhaba Mussa,&nbsp;Tarun Kumar Lohani,&nbsp;Abunu Atlabachew Eshete","doi":"10.1002/gch2.202400137","DOIUrl":null,"url":null,"abstract":"<p>The main objective of this study is to map and evaluate groundwater potential zones (GWPZs) using advanced ensemble machine learning (ML) models, notably Random Forest (RF) and Support Vector Machine (SVM). GWPZs are identified by considering essential factors such as geology, drainage density, slope, land use/land cover (LULC), rainfall, soil, and lineament density. This is combined with datasets used for training and validating the RF and SVM models, which consisted of 75 potential sites (boreholes and springs), 22 non-potential sites (bare lands and settlement areas), and 20 potential sites (water bodies). Each dataset is randomly partitioned into two sets: training (70%) and validation (30%). The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC-ROC). The AUC of the RF model is 0.91, compared to 0.88 for the SVM model. Both models classified GWPZs effectively, but the RF model performed slightly better. The classified GWPZ map shows that high GWPZs are typically located within water bodies, natural springs, low-lying regions, and forested areas. In contrast, low GWPZs are primarily found in shrubland and grassland areas. This study is vital for decision-makers as it promotes sustainable groundwater use and ensures water security in the studied area.</p>","PeriodicalId":12646,"journal":{"name":"Global Challenges","volume":"8 12","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11637779/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Challenges","FirstCategoryId":"103","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/gch2.202400137","RegionNum":4,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

The main objective of this study is to map and evaluate groundwater potential zones (GWPZs) using advanced ensemble machine learning (ML) models, notably Random Forest (RF) and Support Vector Machine (SVM). GWPZs are identified by considering essential factors such as geology, drainage density, slope, land use/land cover (LULC), rainfall, soil, and lineament density. This is combined with datasets used for training and validating the RF and SVM models, which consisted of 75 potential sites (boreholes and springs), 22 non-potential sites (bare lands and settlement areas), and 20 potential sites (water bodies). Each dataset is randomly partitioned into two sets: training (70%) and validation (30%). The model's performance is evaluated using the area under the receiver operating characteristic curve (AUC-ROC). The AUC of the RF model is 0.91, compared to 0.88 for the SVM model. Both models classified GWPZs effectively, but the RF model performed slightly better. The classified GWPZ map shows that high GWPZs are typically located within water bodies, natural springs, low-lying regions, and forested areas. In contrast, low GWPZs are primarily found in shrubland and grassland areas. This study is vital for decision-makers as it promotes sustainable groundwater use and ensures water security in the studied area.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用基于地理信息系统的机器学习集合模型评估埃塞俄比亚吉达博流域的地下水潜力区。
本研究的主要目的是利用先进的集成机器学习(ML)模型,特别是随机森林(RF)和支持向量机(SVM),绘制和评估地下水潜在带(GWPZs)。通过考虑地质、排水密度、坡度、土地利用/土地覆盖(LULC)、降雨量、土壤和地形密度等基本因素来确定gwpz。这与用于训练和验证RF和SVM模型的数据集相结合,其中包括75个潜在地点(钻孔和泉水),22个非潜在地点(裸地和定居区)和20个潜在地点(水体)。每个数据集随机分为两组:训练集(70%)和验证集(30%)。该模型的性能评估使用下面积的接收者工作特征曲线(AUC-ROC)。RF模型的AUC为0.91,而SVM模型的AUC为0.88。两种模型都能有效地分类gwpz,但RF模型表现稍好。GWPZ分类图显示,高GWPZ通常位于水体、天然泉水、低洼地区和森林地区。相比之下,低gwpz主要出现在灌木和草地地区。该研究对决策者至关重要,因为它促进了地下水的可持续利用,并确保了研究区域的水安全。
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 去求助
相关文献
A APLICAÇÃO DE UM JOGO PARA MOTIVAÇÃO DO PROCESSO DE ENSINO-APRENDIZAGEM EM CURSOS DE ENGENHARIA E CIÊNCIAS EXATAS
IF 0 Revista Temas em EducacaoPub Date : 2020-05-30 DOI: 10.22478/ufpb.2359-7003.2020v29n2.51646
Flávia Gonçalves Fernandes, Renato Alejandro Tintaya Mollo, F. D. C. Barbosa
来源期刊
Global Challenges
Global Challenges MULTIDISCIPLINARY SCIENCES-
CiteScore
8.70
自引率
0.00%
发文量
79
审稿时长
16 weeks
期刊最新文献
Issue Information Solar Drying for Domestic and Industrial Applications: A Comprehensive Review of Innovations and Efficiency Enhancements Crop Diversification for Ensuring Sustainable Agriculture, Risk Management and Food Security Mineral Plastics and Gels from Multi-Arm Ionomers Issue Information
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1