从数据到决策:利用 ML 改进孟加拉国的河流泄量预报

Md. Abu Saleh, H.M. Rasel, Briti Ray
{"title":"从数据到决策:利用 ML 改进孟加拉国的河流泄量预报","authors":"Md. Abu Saleh,&nbsp;H.M. Rasel,&nbsp;Briti Ray","doi":"10.1016/j.wsee.2024.09.004","DOIUrl":null,"url":null,"abstract":"<div><div>River discharge forecasting stands at the forefront of environmental management, contributing significantly to sustainable development through its impact on flood prevention, water resource management, ecological conservation, and energy production. This study forecasted the annual river discharge forecasting in the Nilphamari district of Bangladesh, employing random forest (RF), support vector machine (SVM), and gradient boosting machine (GBM) techniques. Historical river discharge data spanning from 1990 to 2020, obtained from eight surface water stations, forms the basis of the analysis. The forecast was performed from 2021 to 2030. 11 statistical parameters were considered for performance evaluation. Additionally, four evaluation plots, comprising a quantile–quantile plot (QQ plot), a residual plot, a Bland Altman plot, and Theil’s U statistic, were employed for a detailed understanding of model accuracy. Results demonstrate that the random forest regression technique exhibited superior accuracy compared to SVM and GBM in training and testing stages. Notably, the coefficient of determination reached 97 % during the testing phase, emphasizing the robustness of this model. While Mean Absolute Error is lower (1085.071 cubic meter per second), in training, the model captures relative changes (Mean Absolute Percentage Error = 0.154) better during prediction. Willmott’s Index in training (0.77) and testing (0.55) suggest the model memorizes training data well and outperforms the other models in testing stage. The findings underscore the efficacy of RF regression as a superior alternative for short-term discharge forecasting, offering valuable insights for integrated water resources management, particularly in flood warning systems and the expansion of irrigation initiatives.</div></div>","PeriodicalId":101280,"journal":{"name":"Watershed Ecology and the Environment","volume":"6 ","pages":"Pages 209-226"},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"From data to decisions: Leveraging ML for improved river discharge forecasting in Bangladesh\",\"authors\":\"Md. Abu Saleh,&nbsp;H.M. Rasel,&nbsp;Briti Ray\",\"doi\":\"10.1016/j.wsee.2024.09.004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>River discharge forecasting stands at the forefront of environmental management, contributing significantly to sustainable development through its impact on flood prevention, water resource management, ecological conservation, and energy production. This study forecasted the annual river discharge forecasting in the Nilphamari district of Bangladesh, employing random forest (RF), support vector machine (SVM), and gradient boosting machine (GBM) techniques. Historical river discharge data spanning from 1990 to 2020, obtained from eight surface water stations, forms the basis of the analysis. The forecast was performed from 2021 to 2030. 11 statistical parameters were considered for performance evaluation. Additionally, four evaluation plots, comprising a quantile–quantile plot (QQ plot), a residual plot, a Bland Altman plot, and Theil’s U statistic, were employed for a detailed understanding of model accuracy. Results demonstrate that the random forest regression technique exhibited superior accuracy compared to SVM and GBM in training and testing stages. Notably, the coefficient of determination reached 97 % during the testing phase, emphasizing the robustness of this model. While Mean Absolute Error is lower (1085.071 cubic meter per second), in training, the model captures relative changes (Mean Absolute Percentage Error = 0.154) better during prediction. Willmott’s Index in training (0.77) and testing (0.55) suggest the model memorizes training data well and outperforms the other models in testing stage. The findings underscore the efficacy of RF regression as a superior alternative for short-term discharge forecasting, offering valuable insights for integrated water resources management, particularly in flood warning systems and the expansion of irrigation initiatives.</div></div>\",\"PeriodicalId\":101280,\"journal\":{\"name\":\"Watershed Ecology and the Environment\",\"volume\":\"6 \",\"pages\":\"Pages 209-226\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Watershed Ecology and the Environment\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2589471424000172\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Watershed Ecology and the Environment","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2589471424000172","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

河流排水量预报处于环境管理的最前沿,通过其对防洪、水资源管理、生态保护和能源生产的影响,为可持续发展做出了重大贡献。本研究采用随机森林(RF)、支持向量机(SVM)和梯度提升机(GBM)技术,对孟加拉国 Nilphamari 地区的年度河流排水量进行了预测。从八个地表水站获得的 1990 年至 2020 年的历史河流排水量数据是分析的基础。预测时间为 2021 年至 2030 年。性能评估考虑了 11 个统计参数。此外,为了详细了解模型的准确性,还采用了四种评价图,包括量化-量化图(QQ 图)、残差图、Bland Altman 图和 Theil's U 统计量。结果表明,在训练和测试阶段,随机森林回归技术的准确性优于 SVM 和 GBM。值得注意的是,在测试阶段,确定系数达到了 97%,强调了该模型的稳健性。虽然平均绝对误差较低(1085.071 立方米/秒),但在训练阶段,该模型能更好地捕捉到预测过程中的相对变化(平均绝对百分比误差 = 0.154)。训练中的威尔莫特指数(0.77)和测试中的威尔莫特指数(0.55)表明,该模型能很好地记忆训练数据,并在测试阶段优于其他模型。研究结果表明,射频回归是短期排泄量预测的一种有效替代方法,为水资源综合管理,特别是洪水预警系统和扩大灌溉范围提供了有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

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
From data to decisions: Leveraging ML for improved river discharge forecasting in Bangladesh
River discharge forecasting stands at the forefront of environmental management, contributing significantly to sustainable development through its impact on flood prevention, water resource management, ecological conservation, and energy production. This study forecasted the annual river discharge forecasting in the Nilphamari district of Bangladesh, employing random forest (RF), support vector machine (SVM), and gradient boosting machine (GBM) techniques. Historical river discharge data spanning from 1990 to 2020, obtained from eight surface water stations, forms the basis of the analysis. The forecast was performed from 2021 to 2030. 11 statistical parameters were considered for performance evaluation. Additionally, four evaluation plots, comprising a quantile–quantile plot (QQ plot), a residual plot, a Bland Altman plot, and Theil’s U statistic, were employed for a detailed understanding of model accuracy. Results demonstrate that the random forest regression technique exhibited superior accuracy compared to SVM and GBM in training and testing stages. Notably, the coefficient of determination reached 97 % during the testing phase, emphasizing the robustness of this model. While Mean Absolute Error is lower (1085.071 cubic meter per second), in training, the model captures relative changes (Mean Absolute Percentage Error = 0.154) better during prediction. Willmott’s Index in training (0.77) and testing (0.55) suggest the model memorizes training data well and outperforms the other models in testing stage. The findings underscore the efficacy of RF regression as a superior alternative for short-term discharge forecasting, offering valuable insights for integrated water resources management, particularly in flood warning systems and the expansion of irrigation initiatives.
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
CiteScore
3.00
自引率
0.00%
发文量
0
期刊最新文献
Simulation of wetland vegetation succession based on coupled Gaussian and population dynamics models: A case study of Poyang Lake wetlands Morphometric analysis of watersheds: A comprehensive review of data sources, quality, and geospatial techniques Flash flood susceptibility mapping of north-east depression of Bangladesh using different GIS based bivariate statistical models Source, fate, toxicity, and remediation of micro-plastic in wetlands: A critical review Effects of Spartina alterniflora control on soil carbon and nitrogen in coastal wetlands
×
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