Predicting Urban Reservoir Levels Using Statistical Learning Techniques

Abstract Urban water supplies are critical to the growth of the city and the wellbeing of its citizens. However, these supplies can be vulnerable to hydrological extremes, such as droughts and floods, especially if they are the main source of water for the city. Maintaining these supplies and prepar...

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Autores principales:	Renee Obringer, Roshanak Nateghi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/002b5383d84f4c929939c7f061ebee37
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spelling	oai:doaj.org-article:002b5383d84f4c929939c7f061ebee372021-12-02T15:07:46ZPredicting Urban Reservoir Levels Using Statistical Learning Techniques10.1038/s41598-018-23509-w2045-2322https://doaj.org/article/002b5383d84f4c929939c7f061ebee372018-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-23509-whttps://doaj.org/toc/2045-2322Abstract Urban water supplies are critical to the growth of the city and the wellbeing of its citizens. However, these supplies can be vulnerable to hydrological extremes, such as droughts and floods, especially if they are the main source of water for the city. Maintaining these supplies and preparing for future conditions is a crucial task for water managers, but predicting hydrological extremes is a challenge. This study tested the abilities of eight statistical learning techniques to predict reservoir levels, given the current hydroclimatic conditions, and provide inferences on the key predictors of reservoir levels. The results showed that random forest, an ensemble, tree-based method, was the best algorithm for predicting reservoir levels. We initially developed the models using Lake Sidney Lanier (Atlanta, Georgia) as the test site; however, further analysis demonstrated that the model based on the random forest algorithm was transferable to other reservoirs, specifically Eagle Creek (Indianapolis, Indiana) and Lake Travis (Austin, Texas). Additionally, we found that although each reservoir was impacted differently, streamflow, city population, and El Niño/Southern Oscillation (ENSO) index were repeatedly among the most important predictors. These are critical variables which can be used by water managers to recognize the potential for reservoir level changes.Renee ObringerRoshanak NateghiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-9 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Renee Obringer Roshanak Nateghi Predicting Urban Reservoir Levels Using Statistical Learning Techniques
description	Abstract Urban water supplies are critical to the growth of the city and the wellbeing of its citizens. However, these supplies can be vulnerable to hydrological extremes, such as droughts and floods, especially if they are the main source of water for the city. Maintaining these supplies and preparing for future conditions is a crucial task for water managers, but predicting hydrological extremes is a challenge. This study tested the abilities of eight statistical learning techniques to predict reservoir levels, given the current hydroclimatic conditions, and provide inferences on the key predictors of reservoir levels. The results showed that random forest, an ensemble, tree-based method, was the best algorithm for predicting reservoir levels. We initially developed the models using Lake Sidney Lanier (Atlanta, Georgia) as the test site; however, further analysis demonstrated that the model based on the random forest algorithm was transferable to other reservoirs, specifically Eagle Creek (Indianapolis, Indiana) and Lake Travis (Austin, Texas). Additionally, we found that although each reservoir was impacted differently, streamflow, city population, and El Niño/Southern Oscillation (ENSO) index were repeatedly among the most important predictors. These are critical variables which can be used by water managers to recognize the potential for reservoir level changes.
format	article
author	Renee Obringer Roshanak Nateghi
author_facet	Renee Obringer Roshanak Nateghi
author_sort	Renee Obringer
title	Predicting Urban Reservoir Levels Using Statistical Learning Techniques
title_short	Predicting Urban Reservoir Levels Using Statistical Learning Techniques
title_full	Predicting Urban Reservoir Levels Using Statistical Learning Techniques
title_fullStr	Predicting Urban Reservoir Levels Using Statistical Learning Techniques
title_full_unstemmed	Predicting Urban Reservoir Levels Using Statistical Learning Techniques
title_sort	predicting urban reservoir levels using statistical learning techniques
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/002b5383d84f4c929939c7f061ebee37
work_keys_str_mv	AT reneeobringer predictingurbanreservoirlevelsusingstatisticallearningtechniques AT roshanaknateghi predictingurbanreservoirlevelsusingstatisticallearningtechniques
_version_	1718388362092478464

Predicting Urban Reservoir Levels Using Statistical Learning Techniques

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