Machine learning in coastal bridge hydrodynamics: A state-of-the-art review

Xu, Guoji; Ji, Chengjie; Xu, Yan; Yu, Enbo; Cao, Zhiyang; Wu, Qinghong; Lin, Ping; Wang, Jinsheng

doi:10.1016/j.apor.2023.103511

Cited by 15 publications

(1 citation statement)

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“…Shamshirband et al [156] proposed a nested grid numerical model that utilizes water depth and surface wind field data for wave height modeling. The combination of hydrodynamic models and machine learning can improve analysis reliability and computational efficiency [157,158]. For example, his-torical data or simulated data from hydrodynamic models can be used to train machine learning models to predict wave heights [159,160], hurricane storm surge hazards [161,162], floods [163], erosion [164], and water level characteristics of storm surges [155].…”

Section: Prediction Of Water Quality Through Coupling Hydrodynamics A...mentioning

confidence: 99%

A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years

Yan,

Zhang,

et al. 2024

JMSE

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Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted in the last five years, we focus on the application of machine learning for predicting water quality. The review begins by presenting the latest methodologies for acquiring water quality data. Categorizing machine learning-based predictions for water quality into two primary segments—indicator prediction and water quality index prediction—further distinguishes between single-indicator and multi-indicator predictions. A meticulous examination of each method’s technical details follows. This article explores current cutting-edge research trends in machine learning algorithms, providing a technical perspective on their application in water quality prediction. It investigates the utilization of algorithms in predicting water quality and concludes by highlighting significant challenges and future research directions. Emphasis is placed on key areas such as hydrodynamic water quality coupling, effective data processing and acquisition, and mitigating model uncertainty. The paper provides a detailed perspective on the present state of application and the principal characteristics of emerging technologies in water quality prediction.

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