An interpretable machine learning model for predicting cavity water depth and cavity length based on XGBoost–SHAP

IF 2.2 3区 工程技术 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Tiexiang Mo, Shanshan Li, Guodong Li
{"title":"An interpretable machine learning model for predicting cavity water depth and cavity length based on XGBoost–SHAP","authors":"Tiexiang Mo, Shanshan Li, Guodong Li","doi":"10.2166/hydro.2023.050","DOIUrl":null,"url":null,"abstract":"\n In contrast to the traditional black box machine learning model, the white box model can achieve higher prediction accuracy and accurately evaluate and explain the prediction results. Cavity water depth and cavity length of aeration facilities are predicted in this research based on Extreme Gradient Boosting (XGBoost) and a Bayesian optimization technique. The Shapley Additive Explanation (SHAP) method is then utilized to explain the prediction results. This study demonstrates how SHAP may order all features and feature interaction terms in accordance with the significance of the input features. The XGBoost–SHAP white box model can reasonably explain the prediction results of XGBoost both globally and locally and can achieve prediction accuracy comparable to the black box model. The cavity water depth and cavity length white box model developed in this study have a promising future application in the shape optimization of aeration facilities and the improvement of model experiments.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":" ","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Hydroinformatics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.2166/hydro.2023.050","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

In contrast to the traditional black box machine learning model, the white box model can achieve higher prediction accuracy and accurately evaluate and explain the prediction results. Cavity water depth and cavity length of aeration facilities are predicted in this research based on Extreme Gradient Boosting (XGBoost) and a Bayesian optimization technique. The Shapley Additive Explanation (SHAP) method is then utilized to explain the prediction results. This study demonstrates how SHAP may order all features and feature interaction terms in accordance with the significance of the input features. The XGBoost–SHAP white box model can reasonably explain the prediction results of XGBoost both globally and locally and can achieve prediction accuracy comparable to the black box model. The cavity water depth and cavity length white box model developed in this study have a promising future application in the shape optimization of aeration facilities and the improvement of model experiments.
基于XGBoost–SHAP的可解释机器学习模型预测空腔水深和空腔长度
与传统的黑盒机器学习模型相比,白盒模型可以实现更高的预测精度,并准确地评估和解释预测结果。基于极限梯度升压(XGBoost)和贝叶斯优化技术,对曝气设施的空腔水深和空腔长度进行了预测。然后利用Shapley加性解释(SHAP)方法来解释预测结果。本研究展示了SHAP如何根据输入特征的重要性对所有特征和特征交互项进行排序。XGBoost–SHAP白盒模型可以在全局和局部合理地解释XGBoost的预测结果,并且可以实现与黑盒模型相当的预测精度。本研究开发的空腔水深和空腔长度白盒模型在曝气设施形状优化和模型实验改进方面具有很好的应用前景。
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
Journal of Hydroinformatics
Journal of Hydroinformatics 工程技术-工程:土木
CiteScore
4.80
自引率
3.70%
发文量
59
审稿时长
3 months
期刊介绍: Journal of Hydroinformatics is a peer-reviewed journal devoted to the application of information technology in the widest sense to problems of the aquatic environment. It promotes Hydroinformatics as a cross-disciplinary field of study, combining technological, human-sociological and more general environmental interests, including an ethical perspective.
×
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