Monthly water demand prediction using wavelet transform, first-order differencing and linear detrending techniques based on multilayer perceptron models

Abdusselam Altunkaynak, Tewodros Assefa Nigussie*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)

Abstract

In this study, combined Discrete Wavelet Transform-Multilayer Perceptron (DWT-MP), combined First-Order Differencing-Multilayer Perceptron (FOD-MP) and combined Linear Detrending-Multilayer Perceptron (LD-MP) were developed and compared with stand-alone Multilayer Perceptron (MP) model for predicting monthly water consumption of Istanbul. The performance of these models were assessed by using coefficient of determination (R2), root mean square error (RMSE) and the Nash-Sutcliffe coefficient of efficiency (CE) as evaluation criteria. The study showed that DWT-MP could be used for forecasting the monthly water demand of Istanbul for only up to prediction lead-time of 3 months. However, FOD-MP was found to perform very well up to 12 months. It can be concluded from the results of the study that First-Order Differencing (FOD) is a reliable pre-processing technique for monthly water demand prediction.

Original languageEnglish
Pages (from-to)177-181
Number of pages5
JournalUrban Water Journal
Volume15
Issue number2
DOIs
Publication statusPublished - 7 Feb 2018

Bibliographical note

Publisher Copyright:
© 2018 Informa UK Limited, trading as Taylor & Francis Group.

Keywords

  • First-order differencing
  • Linear detrending
  • Wavelet Transform

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