Water quality modeling in sewer networks: Review and future research directions

鉴定(生物学) 计算机科学 质量(理念) 预测建模 领域(数学) 城市化 过程(计算) 管理科学 风险分析(工程) 经验模型 数据科学 水质 可转让性 人口 工程类 机器学习 业务 模拟 生态学 操作系统 数学 经济增长 植物 纯数学 经济 人口学 社会学 罗伊特 哲学 生物 认识论
作者
Yueyi Jia,Feifei Zheng,Holger R. Maier,Avi Ostfeld,Enrico Creaco,Dragan Savić,Jeroen Langeveld,Zoran Kapelan
出处
期刊:Water Research [Elsevier BV]
卷期号:202: 117419-117419 被引量:50
标识
DOI:10.1016/j.watres.2021.117419
摘要

Urban sewer networks (SNs) are increasingly facing water quality issues as a result of many challenges, such as population growth, urbanization and climate change. A promising way to addressing these issues is by developing and using water quality models. Many of these models have been developed in recent years to facilitate the management of SNs. Given the proliferation of different water quality models and the promise they have shown, it is timely to assess the state-of-the-art in this field, to identify potential challenges and suggest future research directions. In this review, model types, modeled quality parameters, modeling purpose, data availability, type of case studies and model performance evaluation are critically analyzed and discussed based on a review of 110 papers published between 2010 and 2019. The review identified that applications of empirical and kinetic models dominate those of data-driven models for addressing water quality issues. The majority of models are developed for prediction and process understanding using experimental or field sampled data. While many models have been applied to real problems, the corresponding prediction accuracies are overall moderate or, in some cases, low, especially when dealing with larger SNs. The review also identified the most common issues associated with water quality modeling of SNs and based on these proposed several future research directions. These include the identification of appropriate data resolutions for the development of different SN models, the need and opportunity to develop hybrid SN models and the improvement of SN model transferability.
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