Zubaidi, Salah L. and Kumar, Pavitra and Al-Bugharbee, Hussein and Ahmed, Ali Najah and Ridha, Hussein Mohammed and Mo, Kim Hung and El-Shafie, Ahmed (2023) Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia. Applied Water Science, 13 (9). ISSN 2190-5487, DOI https://doi.org/10.1007/s13201-023-01995-2.
Full text not available from this repository.Abstract
Accurate prediction of short-term water demand, especially, in the case of extreme weather conditions such as flood, droughts and storms, is crucial information for the policy makers to manage the availability of freshwater. This study develops a hybrid model for the prediction of monthly water demand using the database of monthly urban water consumption in Melbourne, Australia. The dataset consisted of minimum, maximum, and mean temperature (& DEG;C), evaporation (mm), rainfall (mm), solar radiation (MJ/m2), maximum relative humidity (%), vapor pressure (hpa), and potential evapotranspiration (mm). The dataset was normalized using natural logarithm and denoized then by employing the discrete wavelet transform. Principle component analysis was used to determine which predictors were most reliable. Hybrid model development included the optimization of ANN coefficients (its weights and biases) using adaptive guided differential evolution algorithm. Post-optimization ANN model was trained using eleven different leaning algorithms. Models were trained several times with different configuration (nodes in hidden layers) to achieve better accuracy. The final optimum learning algorithm was selected based on the performance values (regression; mean absolute, relative and maximum error) and Taylor diagram.
| Item Type: | Article |
|---|---|
| Funders: | Universiti Malaya [Grant No: RMF0360-2021/KW IPPP] |
| Uncontrolled Keywords: | Monthly water demand; Hybrid model; Adaptive guided differential evolution algorithm; Eleven learning algorithms |
| Subjects: | G Geography. Anthropology. Recreation > GE Environmental Sciences |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 01 Nov 2025 12:56 |
| Last Modified: | 01 Nov 2025 12:56 |
| URI: | http://eprints.um.edu.my/id/eprint/48650 |
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