Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia

Autor: Salah L. Zubaidi, Pavitra Kumar, Hussein Al-Bugharbee, Ali Najah Ahmed, Hussein Mohammed Ridha, Kim Hung Mo, Ahmed El-Shafie
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Applied Water Science, Vol 13, Iss 9, Pp 1-10 (2023)
Druh dokumentu: article
ISSN: 2190-5487
2190-5495
32541341
DOI: 10.1007/s13201-023-01995-2
Popis: 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 (°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.
Databáze: Directory of Open Access Journals
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