Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods

<p>Sustainable urban drainage systems (SuDS) are decentralized stormwater management practices that mimic natural drainage processes. The hydrological processes of SuDS are often modeled using process-based models. However, it can require considerable effort to set up these models. This study...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Y. Yang, T. F. M. Chui
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Technology T Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation G Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/d74218fc57844b30b8cd618e99c0c64b
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:d74218fc57844b30b8cd618e99c0c64b
record_format	dspace
spelling	oai:doaj.org-article:d74218fc57844b30b8cd618e99c0c64b2021-11-11T06:54:24ZModeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods10.5194/hess-25-5839-20211027-56061607-7938https://doaj.org/article/d74218fc57844b30b8cd618e99c0c64b2021-11-01T00:00:00Zhttps://hess.copernicus.org/articles/25/5839/2021/hess-25-5839-2021.pdfhttps://doaj.org/toc/1027-5606https://doaj.org/toc/1607-7938<p>Sustainable urban drainage systems (SuDS) are decentralized stormwater management practices that mimic natural drainage processes. The hydrological processes of SuDS are often modeled using process-based models. However, it can require considerable effort to set up these models. This study thus proposes a machine learning (ML) method to directly learn the statistical correlations between the hydrological responses of SuDS and the forcing variables at sub-hourly timescales from observation data. The proposed methods are applied to two SuDS catchments with different sizes, SuDS practice types, and data availabilities in the USA for discharge prediction. The resulting models have high prediction accuracies (Nash–Sutcliffe efficiency, NSE, <span class="inline-formula"><i>></i>0.70</span>). ML explanation methods are then employed to derive the basis of each ML prediction, based on which the hydrological processes being modeled are then inferred. The physical realism of the inferred hydrological processes is then compared to that would be expected based on the domain-specific knowledge of the system being modeled. The inferred processes of some models, however, are found to be physically implausible. For instance, negative contributions of rainfall to runoff have been identified in some models. This study further empirically shows that an ML model's ability to provide accurate predictions can be uncorrelated with its ability to offer plausible explanations to the physical processes being modeled. Finally, this study provides a high-level overview of the practices of inferring physical processes from the ML modeling results and shows both conceptually and empirically that large uncertainty exists in every step of the inference processes. In summary, this study shows that ML methods are a useful tool for predicting the hydrological responses of SuDS catchments, and the hydrological processes inferred from modeling results should be interpreted cautiously due to the existence of large uncertainty in the inference processes.</p>Y. YangT. F. M. ChuiCopernicus PublicationsarticleTechnologyTEnvironmental technology. Sanitary engineeringTD1-1066Geography. Anthropology. RecreationGEnvironmental sciencesGE1-350ENHydrology and Earth System Sciences, Vol 25, Pp 5839-5858 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation G Environmental sciences GE1-350
spellingShingle	Technology T Environmental technology. Sanitary engineering TD1-1066 Geography. Anthropology. Recreation G Environmental sciences GE1-350 Y. Yang T. F. M. Chui Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods
description	<p>Sustainable urban drainage systems (SuDS) are decentralized stormwater management practices that mimic natural drainage processes. The hydrological processes of SuDS are often modeled using process-based models. However, it can require considerable effort to set up these models. This study thus proposes a machine learning (ML) method to directly learn the statistical correlations between the hydrological responses of SuDS and the forcing variables at sub-hourly timescales from observation data. The proposed methods are applied to two SuDS catchments with different sizes, SuDS practice types, and data availabilities in the USA for discharge prediction. The resulting models have high prediction accuracies (Nash–Sutcliffe efficiency, NSE, <span class="inline-formula"><i>></i>0.70</span>). ML explanation methods are then employed to derive the basis of each ML prediction, based on which the hydrological processes being modeled are then inferred. The physical realism of the inferred hydrological processes is then compared to that would be expected based on the domain-specific knowledge of the system being modeled. The inferred processes of some models, however, are found to be physically implausible. For instance, negative contributions of rainfall to runoff have been identified in some models. This study further empirically shows that an ML model's ability to provide accurate predictions can be uncorrelated with its ability to offer plausible explanations to the physical processes being modeled. Finally, this study provides a high-level overview of the practices of inferring physical processes from the ML modeling results and shows both conceptually and empirically that large uncertainty exists in every step of the inference processes. In summary, this study shows that ML methods are a useful tool for predicting the hydrological responses of SuDS catchments, and the hydrological processes inferred from modeling results should be interpreted cautiously due to the existence of large uncertainty in the inference processes.</p>
format	article
author	Y. Yang T. F. M. Chui
author_facet	Y. Yang T. F. M. Chui
author_sort	Y. Yang
title	Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods
title_short	Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods
title_full	Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods
title_fullStr	Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods
title_full_unstemmed	Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods
title_sort	modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods
publisher	Copernicus Publications
publishDate	2021
url	https://doaj.org/article/d74218fc57844b30b8cd618e99c0c64b
work_keys_str_mv	AT yyang modelingandinterpretinghydrologicalresponsesofsustainableurbandrainagesystemswithexplainablemachinelearningmethods AT tfmchui modelingandinterpretinghydrologicalresponsesofsustainableurbandrainagesystemswithexplainablemachinelearningmethods
_version_	1718439446254190592

Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods

Ejemplares similares