Prediction of multi-sectoral longitudinal water withdrawals using hierarchical machine learning models

IF 2.2 3区 工程技术 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
J. Shortridge
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引用次数: 0

Abstract

Accurate models of water withdrawal are crucial in anticipating the potential water use impacts of drought and climate change. Machine learning methods can simulate the complex, nonlinear relationship between water use and potential explanatory factors, but rarely incorporate the hierarchical nature of water use data. This work presents a novel approach for the prediction of water withdrawals across multiple usage sectors using an ensemble of models fit at different hierarchical levels. Models were fit at the facility and sectoral grouping levels, as well as across facility clusters defined by temporal water use characteristics. Using repeated holdout cross-validation and a dataset of over 300,000 observations of monthly water withdrawal across 1,509 facilities, it demonstrates that ensemble predictions led to statistically significant improvements in predictive performance in five of the eight sectors analyzed. The use of ensemble modeling resulted in lower predictive errors compared to facility models in 65% of facilities analyzed. The relative improvement gained by ensemble modeling was greatest for facilities with fewer observations and higher variance, indicating its potential value in predicting withdrawal for facilities with relatively short data records or data quality issues.
利用分层机器学习模型预测多部门纵向取水
准确的取水模型对于预测干旱和气候变化对水资源利用的潜在影响至关重要。机器学习方法可以模拟用水和潜在解释因素之间复杂的非线性关系,但很少纳入用水数据的层次性质。这项工作提出了一种新的方法,用于预测跨多个使用部门的取水量,使用不同层次水平的模型集合。模型适用于设施和部门分组级别,以及按时间用水特征定义的设施集群。通过反复的交叉验证和对1509个设施每月取水量的30多万次观察数据集,该研究表明,在分析的8个部门中,有5个部门的集合预测在统计上显着提高了预测性能。在分析的65%的设施中,与设施模型相比,集成模型的使用导致了更低的预测误差。对于观测值较少、方差较大的设施,集成建模获得的相对改进最大,这表明它在预测数据记录相对较短或数据质量问题的设施撤离方面具有潜在价值。
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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.
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