伊朗东北部Sabzevar盆地Kal-e Shur河悬沙荷载建模、评价与预测

IF 5.7 3区 环境科学与生态学 Q1 WATER RESOURCES
M. A. Zangeneh Asadi, L. Goli Mokhtari, R. Zandi, M. Naemitabar
{"title":"伊朗东北部Sabzevar盆地Kal-e Shur河悬沙荷载建模、评价与预测","authors":"M. A. Zangeneh Asadi,&nbsp;L. Goli Mokhtari,&nbsp;R. Zandi,&nbsp;M. Naemitabar","doi":"10.1007/s13201-025-02361-0","DOIUrl":null,"url":null,"abstract":"<div><p>Studying sediment transport to rivers is crucial for effective river management, engineering, and environmental preservation. Neglecting this aspect can lead to significant harm to natural ecosystems. This research aims to estimate suspended sediment levels in the Kal-e Shur Sabzevar River using various machine learning algorithms, which have gained popularity in recent years due to their high accuracy and reliability. The study employs ensemble Bagging algorithms, the gradient boosting machine (GBM), genetic algorithm, Naïve Bayes algorithm, gradient boosting decision trees, and extremely randomized trees. These algorithms provide a coherent framework that can serve as a standard for evaluating and comparing models in future research. Initially, data from 354 sediment measurement stations, including flow discharge, sediment discharge, and precipitation, were collected. After validating data homogeneity using the double mass method, 70% of the data were allocated for training, and 30% for testing. The algorithms were trained with this data, and their performance was evaluated using the coefficient of determination (<i>R</i><sup>2</sup>), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) statistics. Additionally, a partial least squares (PLS) regression model was employed to identify the most influential factors affecting suspended sediment load in the basin. The results demonstrate that the gradient boosting machine (GBM) model outperforms other algorithms, exhibiting <i>R</i><sup>2</sup> values of 0.95, RMSE values of 0.019, and NSE values of 0.78. The PLS model identified geological factors and slope as primary determinants of suspended sediment load in the region. Lastly, the algorithms predicted sediment levels, with the GBM algorithm estimating a sediment concentration of 8955 mg/liter with a relative error of 8.54%, indicating strong alignment with the total sediment load in the region.</p></div>","PeriodicalId":8374,"journal":{"name":"Applied Water Science","volume":"15 3","pages":""},"PeriodicalIF":5.7000,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s13201-025-02361-0.pdf","citationCount":"0","resultStr":"{\"title\":\"Modeling, evaluation and forecasting of suspended sediment load in Kal-e Shur River, Sabzevar Basin, in northeast of Iran\",\"authors\":\"M. A. Zangeneh Asadi,&nbsp;L. Goli Mokhtari,&nbsp;R. Zandi,&nbsp;M. Naemitabar\",\"doi\":\"10.1007/s13201-025-02361-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Studying sediment transport to rivers is crucial for effective river management, engineering, and environmental preservation. Neglecting this aspect can lead to significant harm to natural ecosystems. This research aims to estimate suspended sediment levels in the Kal-e Shur Sabzevar River using various machine learning algorithms, which have gained popularity in recent years due to their high accuracy and reliability. The study employs ensemble Bagging algorithms, the gradient boosting machine (GBM), genetic algorithm, Naïve Bayes algorithm, gradient boosting decision trees, and extremely randomized trees. These algorithms provide a coherent framework that can serve as a standard for evaluating and comparing models in future research. Initially, data from 354 sediment measurement stations, including flow discharge, sediment discharge, and precipitation, were collected. After validating data homogeneity using the double mass method, 70% of the data were allocated for training, and 30% for testing. The algorithms were trained with this data, and their performance was evaluated using the coefficient of determination (<i>R</i><sup>2</sup>), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) statistics. Additionally, a partial least squares (PLS) regression model was employed to identify the most influential factors affecting suspended sediment load in the basin. The results demonstrate that the gradient boosting machine (GBM) model outperforms other algorithms, exhibiting <i>R</i><sup>2</sup> values of 0.95, RMSE values of 0.019, and NSE values of 0.78. The PLS model identified geological factors and slope as primary determinants of suspended sediment load in the region. Lastly, the algorithms predicted sediment levels, with the GBM algorithm estimating a sediment concentration of 8955 mg/liter with a relative error of 8.54%, indicating strong alignment with the total sediment load in the region.</p></div>\",\"PeriodicalId\":8374,\"journal\":{\"name\":\"Applied Water Science\",\"volume\":\"15 3\",\"pages\":\"\"},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2025-02-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s13201-025-02361-0.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Water Science\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s13201-025-02361-0\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"WATER RESOURCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Water Science","FirstCategoryId":"93","ListUrlMain":"https://link.springer.com/article/10.1007/s13201-025-02361-0","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"WATER RESOURCES","Score":null,"Total":0}
引用次数: 0

摘要

研究泥沙向河流的运移对有效的河流管理、工程和环境保护至关重要。忽视这一方面会对自然生态系统造成重大危害。这项研究旨在使用各种机器学习算法估计Kal-e Shur Sabzevar河中的悬浮沉积物水平,这些算法近年来因其高精度和可靠性而受到欢迎。本研究采用了集合Bagging算法、梯度增强机(GBM)、遗传算法、Naïve贝叶斯算法、梯度增强决策树和极度随机树。这些算法提供了一个连贯的框架,可以作为未来研究中评估和比较模型的标准。最初,收集了354个泥沙测量站的数据,包括流量、输沙量和降水。使用双质量法验证数据同质性后,分配70%的数据用于训练,30%用于测试。使用这些数据对算法进行训练,并使用决定系数(R2)、均方根误差(RMSE)和Nash-Sutcliffe效率(NSE)统计量对算法的性能进行评估。此外,采用偏最小二乘(PLS)回归模型识别影响流域悬沙负荷的最主要因素。结果表明,梯度增强机(GBM)模型的R2值为0.95,RMSE值为0.019,NSE值为0.78,优于其他算法。PLS模型确定了地质因素和坡度是该地区悬沙荷载的主要决定因素。最后,采用GBM算法对该区域的含沙量进行预测,预测含沙量为8955 mg/l,相对误差为8.54%,与该区域的总含沙量具有较强的一致性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling, evaluation and forecasting of suspended sediment load in Kal-e Shur River, Sabzevar Basin, in northeast of Iran

Studying sediment transport to rivers is crucial for effective river management, engineering, and environmental preservation. Neglecting this aspect can lead to significant harm to natural ecosystems. This research aims to estimate suspended sediment levels in the Kal-e Shur Sabzevar River using various machine learning algorithms, which have gained popularity in recent years due to their high accuracy and reliability. The study employs ensemble Bagging algorithms, the gradient boosting machine (GBM), genetic algorithm, Naïve Bayes algorithm, gradient boosting decision trees, and extremely randomized trees. These algorithms provide a coherent framework that can serve as a standard for evaluating and comparing models in future research. Initially, data from 354 sediment measurement stations, including flow discharge, sediment discharge, and precipitation, were collected. After validating data homogeneity using the double mass method, 70% of the data were allocated for training, and 30% for testing. The algorithms were trained with this data, and their performance was evaluated using the coefficient of determination (R2), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) statistics. Additionally, a partial least squares (PLS) regression model was employed to identify the most influential factors affecting suspended sediment load in the basin. The results demonstrate that the gradient boosting machine (GBM) model outperforms other algorithms, exhibiting R2 values of 0.95, RMSE values of 0.019, and NSE values of 0.78. The PLS model identified geological factors and slope as primary determinants of suspended sediment load in the region. Lastly, the algorithms predicted sediment levels, with the GBM algorithm estimating a sediment concentration of 8955 mg/liter with a relative error of 8.54%, indicating strong alignment with the total sediment load in the region.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Applied Water Science
Applied Water Science WATER RESOURCES-
CiteScore
9.90
自引率
3.60%
发文量
268
审稿时长
13 weeks
期刊介绍:
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信