Water demand forecasting: review of soft computing methods.

Abstract:

:Demand forecasting plays a vital role in resource management for governments and private companies. Considering the scarcity of water and its inherent constraints, demand management and forecasting in this domain are critically important. Several soft computing techniques have been developed over the last few decades for water demand forecasting. This study focuses on soft computing methods of water consumption forecasting published between 2005 and 2015. These methods include artificial neural networks (ANNs), fuzzy and neuro-fuzzy models, support vector machines, metaheuristics, and system dynamics. Furthermore, it was discussed that while in short-term forecasting, ANNs have been superior in many cases, but it is still very difficult to pick a single method as the overall best. According to the literature, various methods and their hybrids are applied to water demand forecasting. However, it seems soft computing has a lot more to contribute to water demand forecasting. These contribution areas include, but are not limited, to various ANN architectures, unsupervised methods, deep learning, various metaheuristics, and ensemble methods. Moreover, it is found that soft computing methods are mainly used for short-term demand forecasting.

journal_name

Environ Monit Assess

authors

Ghalehkhondabi I,Ardjmand E,Young WA 2nd,Weckman GR

doi

10.1007/s10661-017-6030-3

subject

Has Abstract

pub_date

2017-07-01 00:00:00

pages

313

issue

7

eissn

0167-6369

issn

1573-2959

pii

10.1007/s10661-017-6030-3

journal_volume

189

pub_type

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