Water demand forecasting based on adaptive extreme learning machine

DOI

10.2991/icaise.2013.10How to use a DOI?

Keywords

Water consumption, Extreme learning machine,Forecasting, Time series

Abstract

Predicting water consumption is of key importance for water supply management, which is also relevant in processes for reviewing prices.In this study, a hybrid method based on extreme learning machine model with the adaptive metrics of inputs is proposed for improving forecasting accuracy. The adaptive metrics of inputs can solve the problems of amplitude changing and trend determination, and reduce the effect of the overfitting of networks. It was found that the proposed model is practical for water demand forecasting and outperforms the autoregression (AR), artificial neural network (ANN), support vector machine(SVM) and extreme learning machine (ELM) models.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jinming Jia
AU  - Shengyue Hao
PY  - 2013/08
DA  - 2013/08
TI  - Water demand forecasting based on adaptive extreme learning machine
BT  - Proceedings of the 2013 The International Conference on Artificial Intelligence and Software Engineering (ICAISE 2013)
PB  - Atlantis Press
SP  - 42
EP  - 45
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaise.2013.10
DO  - 10.2991/icaise.2013.10
ID  - Jia2013/08
ER  -