Short-term forecasting of monthly water consumption in hyper-arid climate using recurrent neural networks
Author
Source
Journal of Engineering Research
Issue
Vol. 9, Issue 3 B (30 Sep. 2021), pp.56-69, 14 p.
Publisher
Kuwait University Academic Publication Council
Publication Date
2021-09-30
Country of Publication
Kuwait
No. of Pages
14
Main Subjects
Abstract EN
Freshwater supply is a major challenge in regions with limited water resources and extremely arid climatic conditions.
The objective of this study is to model the monthly water demand in Kuwait using the nonlinear autoregressive with exogenous input (NARX) neural network approach.
The country lacks conventional surface water resources and is characterized by extremely arid climate.
In addition, it has one of the fastest growing populations.
In this study, linear detrending is performed on the water consumption time series for the period from January 1993 to December 2018 to eliminate the influence of population growth before application to the NARX model.
Monthly temperature data are selected as exogenous input to the NARX model, because they are strongly associated with the water consumption data.
Correlation analyses are performed to determine the input and feedback delays of the NARX model.
The results demonstrate that the recurrent NARX model is efficient and robust for forecasting the short-term water demand, with a Nash-Sutcliffe (NS) coefficient of 0.837 in the validation period.
Seasonal model assessment shows that the model performs best during the critical summer season.
The NARX-based recurrent model is established as a powerful and promising tool for predicting urban water demand.
Thus, it can efficiently aid the development of resilient water supply plans.
American Psychological Association (APA)
al-Sumiae, Abd Allah A.. 2021. Short-term forecasting of monthly water consumption in hyper-arid climate using recurrent neural networks. Journal of Engineering Research،Vol. 9, no. 3 B, pp.56-69.
https://search.emarefa.net/detail/BIM-1494982
Modern Language Association (MLA)
al-Sumiae, Abd Allah A.. Short-term forecasting of monthly water consumption in hyper-arid climate using recurrent neural networks. Journal of Engineering Research Vol. 9, no. 3 B (Sep. 2021), pp.56-69.
https://search.emarefa.net/detail/BIM-1494982
American Medical Association (AMA)
al-Sumiae, Abd Allah A.. Short-term forecasting of monthly water consumption in hyper-arid climate using recurrent neural networks. Journal of Engineering Research. 2021. Vol. 9, no. 3 B, pp.56-69.
https://search.emarefa.net/detail/BIM-1494982
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 68-69
Record ID
BIM-1494982