Open AccessSimulating a Stochastic Signal of Urban Water Demand by a Novel Combination of Data Analytic and Machine Learning Techniques
Author(s) -
Salah L. Zubaidi,
Hussein Al-Bugharbee,
Yousif Raad Muhsin,
Sadik Kamel Gharghan,
Khalid S. Hashim,
Hussein Mohammed Ridha,
Rafid Alkhaddar,
Patryk Kot,
Mawada Abdellatif
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1058/1/012066
Subject(s) - artificial neural network , hilbert–huang transform , computer science , data pre processing , preprocessor , signal (programming language) , time series , series (stratigraphy) , mode (computer interface) , approximation error , algorithm , artificial intelligence , data mining , mathematical optimization , machine learning , mathematics , white noise , telecommunications , paleontology , biology , programming language , operating system
In this research, a new methodology is presented to forecast the stochastic component of urban water demand for Baghdad City from 2003 to 2014. The methodology contains data preprocessing to analyse raw time series of water via Empirical Mode Decomposition (EMD) technique and select the best scenario of independent variables by a stepwise regression method. Artificial neural network (ANN) is integrated by Backtracking Search Algorithm (BSA) to find the best factors of the ANN model. The outcomes reveal that data pre-processing can detect the stochastic signal of water data and choice the best model input’s scenario. BSA successfully determines the parameters of the ANN model. The methodology accurately simulated the stochastic signal of water time series depend on different statistical criteria such as coefficient of determination and mean absolute relative error equal to 0.99 and 0.0208, respectively.