Abstract
AbstractIn 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.
Funder
Karadeniz Technical University
Publisher
Springer Science and Business Media LLC
Reference55 articles.
1. Republic of Turkey Ministry of Environment, Urbanization and Climate Change. https://webdosya.csb.gov.tr/db/bolu/icerikler/su-20180222083149.pdf. Accessed on 20th Aug
2. Istanbul Metropolitan Municipality Water Works and Canalization Administration (ISKI). https://iski.Istanbul/kurumsal/hakkimizda/su-kaynaklari/. Accessed on 20th Aug
3. Turkish Statistical Institue (TURKSTAT). https://data.tuik.gov.tr/Bulten/Index?p=The-Results-of-Address-Based-Population-Registration-System-2021-45500&dil=2. Accessed on 20th Aug
4. Haque, M.M.; Souza, A.; Rahman, A.: Water demand modelling using independent component regression technique. Water Resour. Manag. 31, 299–312 (2017). https://doi.org/10.1007/s11269-016-1525-1
5. Altunkaynak, A.; Nigussie, T.A.: Monthly water consumption prediction using season algorithm and wavelet transform-based models. J. Water Resour. Plan. Manag. 143, 04017011 (2017). https://doi.org/10.1061/(ASCE)WR.1943-5452.0000761