We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Modeling and Forecasting of Water Demand in the City of Istanbul Using Artificial Neural Networks Optimized with Rao Algorithms.
- Authors
Uzlu, Ergun
- Abstract
In this study, a hybrid artificial neural network (ANN)-Rao series (Rao_1, Rao_2, and Rao_3) algorithm model was developed to analyze water consumption in Istanbul province, Turkey. A multiple linear regression (MLR) model was developed and an ANN was also trained with back-propagation (BP) artificial bee colony (ABC) algorithms for comparison. Gross domestic product and population data were treated as independent variables. To test the accuracy of the presently developed hybrid model, its outputs were compared with those of ANN-BP, ANN-ABC, and MLR models. Error values calculated for the test set indicated that the ANN-Rao_3 algorithm outperformed the MLR, ANN-BP, and ANN-ABC reference models as well as ANN-Rao_1 and ANN-Rao_2 algorithms. Therefore, using the ANN-Rao_3 model, water consumption forecasts for Istanbul province were generated out to 2035 for low-, expected-, and high-water demand conditions. The model-generated forecasts indicate that the water requirements of Istanbul in 2035 will be between 1182.95 and 1399.54 million m3, with the upper-range estimates outpacing supplies. According to low and expected scenarios, there will be no problem in providing the water needs of Istanbul until 2035. However, according to high scenario, water needs of Istanbul will not be provided as of 2033.Therefore, water conservation policies should be enacted to ensure provision of the water needs of Istanbul province from 2033 onward.
- Subjects
ISTANBUL (Turkey); ARTIFICIAL neural networks; WATER conservation; GROSS domestic product; INDEPENDENT variables; ALGORITHMS; DEMAND forecasting
- Publication
Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ), 2024, Vol 49, Issue 10, p13477
- ISSN
2193-567X
- Publication type
Article
- DOI
10.1007/s13369-023-08683-y