Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River

IF 5.9 1区工程技术 Q1 ENGINEERING, MECHANICAL

Engineering Applications of Computational Fluid Mechanics Pub Date : 2024-01-23 DOI:10.1080/19942060.2024.2304094

Shuguang Li, Sultan Noman Qasem, Shahab S. Band, Rasoul Ameri, Hao-Ting Pai, Saeid Mehdizadeh

引用次数: 0

Abstract

Monitoring the quality of river water is of fundamental importance and needs to be taken into consideration when it comes to the research into the hydrological field. In this context, the concentra...

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用于估算图拉丁河每日溶解氧浓度的可解释机器学习模型

监测河水水质至关重要，在水文领域开展研究时必须加以考虑。在这种情况下，对河水的浓度进行监测就显得尤为重要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Engineering Applications of Computational Fluid Mechanics ENGINEERING, MULTIDISCIPLINARY-ENGINEERING, MECHANICAL

CiteScore

10.60

自引率

14.80%

发文量

109

审稿时长

3.4 months

期刊介绍： The aim of Engineering Applications of Computational Fluid Mechanics is a continuous and timely dissemination of innovative, practical and industrial applications of computational techniques to solve the whole range of hitherto intractable fluid mechanics problems. The journal is a truly interdisciplinary forum and publishes original contributions on the latest advances in numerical methods in fluid mechanics and their applications to various engineering fields including aeronautic, civil, environmental, hydraulic and mechanical. The journal has a distinctive and balanced international contribution, with emphasis on papers addressing practical problem-solving by means of robust numerical techniques to generate precise flow prediction and optimum design, and those fostering the thorough understanding of the physics of fluid motion. It is an open access journal.