Approaches for unsupervised identification of data-driven models for flow forecasting in urban drainage systems

In this work, an unsupervised model selection procedure for identifying data-driven forecast models for urban drainage systems is proposed and evaluated. Specifically, we consider the case of predicting inflows to wastewater treatment plants for activating wet weather operation (aeration tank settli...

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Autores principales:	Ari Jóhannesson, Luca Vezzaro, Peter Steen Mikkelsen, Roland Löwe
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	arimax-type models influent forecasting time-series modelling urban hydrology Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/35c8a2c4025e4166b900eab326a609ec
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spelling	oai:doaj.org-article:35c8a2c4025e4166b900eab326a609ec2021-11-23T18:48:53ZApproaches for unsupervised identification of data-driven models for flow forecasting in urban drainage systems1464-71411465-173410.2166/hydro.2021.020https://doaj.org/article/35c8a2c4025e4166b900eab326a609ec2021-11-01T00:00:00Zhttp://jh.iwaponline.com/content/23/6/1368https://doaj.org/toc/1464-7141https://doaj.org/toc/1465-1734In this work, an unsupervised model selection procedure for identifying data-driven forecast models for urban drainage systems is proposed and evaluated. Specifically, we consider the case of predicting inflows to wastewater treatment plants for activating wet weather operation (aeration tank settling, ATS) using Box–Jenkins models. The model selection procedure considers different model structures and different objective functions. The hyperparameter search space is constrained based on the time of concentration in the catchment. Objective function criteria that minimize one-step-ahead as well as multi-step prediction errors are considered. Finally, we consider two criteria for unsupervised selection of the best-performing model. These measure the agreement of observed and predicted hydrographs (persistence index), as well as the binary exceedance of critical flow thresholds (critical success index (CSI)). Our work shows that forecast models can be developed in an unsupervised manner, and ATS activation is correctly forecasted in 60–90% of the events. The selected model structures reflect the physical behaviour of the catchment. Models should not be selected on operational criteria like the CSI due to a risk of overfitting. The degree to which rainfall input improves forecasts depends on the specific catchment, and the objective function criterion that should be used for coefficient estimation depends on the application context. HIGHLIGHTS Unsupervised model selection procedure for influent forecasting with ARIMA-type models.; Development of hyperparameter selection criteria, i.e., physical/operational decision criterion.; Comparisons of objective function criteria, i.e., single/multi-step forecasts.; Evaluation of the impact of rainfall input on flow forecast quality.;Ari JóhannessonLuca VezzaroPeter Steen MikkelsenRoland LöweIWA Publishingarticlearimax-type modelsinfluent forecastingtime-series modellingurban hydrologyInformation technologyT58.5-58.64Environmental technology. Sanitary engineeringTD1-1066ENJournal of Hydroinformatics, Vol 23, Iss 6, Pp 1368-1381 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	arimax-type models influent forecasting time-series modelling urban hydrology Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
spellingShingle	arimax-type models influent forecasting time-series modelling urban hydrology Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066 Ari Jóhannesson Luca Vezzaro Peter Steen Mikkelsen Roland Löwe Approaches for unsupervised identification of data-driven models for flow forecasting in urban drainage systems
description	In this work, an unsupervised model selection procedure for identifying data-driven forecast models for urban drainage systems is proposed and evaluated. Specifically, we consider the case of predicting inflows to wastewater treatment plants for activating wet weather operation (aeration tank settling, ATS) using Box–Jenkins models. The model selection procedure considers different model structures and different objective functions. The hyperparameter search space is constrained based on the time of concentration in the catchment. Objective function criteria that minimize one-step-ahead as well as multi-step prediction errors are considered. Finally, we consider two criteria for unsupervised selection of the best-performing model. These measure the agreement of observed and predicted hydrographs (persistence index), as well as the binary exceedance of critical flow thresholds (critical success index (CSI)). Our work shows that forecast models can be developed in an unsupervised manner, and ATS activation is correctly forecasted in 60–90% of the events. The selected model structures reflect the physical behaviour of the catchment. Models should not be selected on operational criteria like the CSI due to a risk of overfitting. The degree to which rainfall input improves forecasts depends on the specific catchment, and the objective function criterion that should be used for coefficient estimation depends on the application context. HIGHLIGHTS Unsupervised model selection procedure for influent forecasting with ARIMA-type models.; Development of hyperparameter selection criteria, i.e., physical/operational decision criterion.; Comparisons of objective function criteria, i.e., single/multi-step forecasts.; Evaluation of the impact of rainfall input on flow forecast quality.;
format	article
author	Ari Jóhannesson Luca Vezzaro Peter Steen Mikkelsen Roland Löwe
author_facet	Ari Jóhannesson Luca Vezzaro Peter Steen Mikkelsen Roland Löwe
author_sort	Ari Jóhannesson
title	Approaches for unsupervised identification of data-driven models for flow forecasting in urban drainage systems
title_short	Approaches for unsupervised identification of data-driven models for flow forecasting in urban drainage systems
title_full	Approaches for unsupervised identification of data-driven models for flow forecasting in urban drainage systems
title_fullStr	Approaches for unsupervised identification of data-driven models for flow forecasting in urban drainage systems
title_full_unstemmed	Approaches for unsupervised identification of data-driven models for flow forecasting in urban drainage systems
title_sort	approaches for unsupervised identification of data-driven models for flow forecasting in urban drainage systems
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/35c8a2c4025e4166b900eab326a609ec
work_keys_str_mv	AT arijohannesson approachesforunsupervisedidentificationofdatadrivenmodelsforflowforecastinginurbandrainagesystems AT lucavezzaro approachesforunsupervisedidentificationofdatadrivenmodelsforflowforecastinginurbandrainagesystems AT petersteenmikkelsen approachesforunsupervisedidentificationofdatadrivenmodelsforflowforecastinginurbandrainagesystems AT rolandlowe approachesforunsupervisedidentificationofdatadrivenmodelsforflowforecastinginurbandrainagesystems
_version_	1718416144176513024

Approaches for unsupervised identification of data-driven models for flow forecasting in urban drainage systems

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