Prediction of Daily Water Demand of the Shiraz City Using Neural Network and Honey Bee Colony Optimization Algorithm

Document Type : Applied Article

Authors

• Elham Asrari ¹
• Mohammd Hosseini ²

¹ Associate Professor, Department of Civil Engineering, Payame Noor University, Tehran, Iran

² M.Sc, Department of Civil Engineering, Payame Noor University, Tehran, Iran

10.22067/jwsd.v9i2.2201.1117

Abstract

Access to safe drinking water is one of the most important human needs and citizenship rights. For this reason, the supply, transmission, treatment, and distribution of sanitary drinking water to meet the water needs of urban and rural subscribers is one of the primary tasks and priorities of any government. Predicting water demand in water supply and distribution systems will be of great help to managers related to water supply, to manage and prevent crises and water supply planning, service and maintenance of equipment and facilities, culture, information and so on. In this study, a combined method based on neural network methods and bee colony optimization to predict drinking water demand and health of Shiraz is presented. The purpose of this study was to improve the accuracy of water demand forecasting using the neural network method. Parameters considered for modeling water demand forecasts include past information on water demand, air temperature, population, wind, and date. The data used to train the neural network included 10 years from 1988 to 1997. In order to verify and evaluate the performance of the proposed method, water demand in 1998 and April 1999 has been predicted and compared with real statistics. Based on the obtained results, it was concluded that the proposed method was able to adequately predict water demand. The proposed method has good accuracy and the deviation of the water demand forecast in the worst conditions has reached one percent, which is an acceptable amount. Statistically, the results obtained using the MAPE parameter were compared with previous studies, and from this perspective, the proposed method is reliable and has good efficiency in predicting the water demand of the Shiraz city system.

Keywords

• Water Supply System Upgrade
• Water Demand Forecas
• Shiraz
• Neural Network

Main Subjects

• Risk, Sustainability and Resilience
• Water Management and Economy

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References

Abiodun O. I., Jantan A., Omolara A. E., Dada K. V., Mohamed N. A and Arshad H. 2018. State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11): e00938.
Bakker H., van Duistc K., van Schagen K., Vreeburg J. and Rietved L. 2014. Improving the performance of water demand forecasting models by using weather input, Procedia Engineering, 70: 93-103.
Candelieri A. 2017. Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection. Water, (3): 224-243.
Chen S., Guohua F., Xianfeng H. and Yuhong Zh. 2018. Water Quality Prediction Model of a Water Diversion Project Based on the Improved Artificial Bee Colony–Back propagation Neural Network, Water, 10(6): 806.        
Ghalehkhondabi I., Ardjmand E. and Young W.A. 2017. Water demand forecasting: review of soft computing methods. Environmental Monitoring and Assessment Journal, 189(7): 313-330.
Graf R., Zhu S. and Sivakumar B. 2019. Forecasting river water temperature time series using a wavelet–neural network hybrid modelling approach. Journal of Hydrology, 578: 124115.
Goldberg David E. 1989. Genetic algorithms in search, optimization and machine learning, 1st ed. Addison-Wesley Longman Publishing Co., Inc, Boston.
Guo L., Fu P., Shi T., Chen Y., Zeng C., Zhang H. and Wang S. 2021. Exploring influence factors in mapping soil organic carbon on low-relief agricultural lands using time series of remote sensing data. Soil and Tillage Research, 210: 104982.
Hutton C., Hofman J. and Kapelan Z. 2020. Water demand forecasting accuracy and influencing factors at different spatial scales using a Gradient Boosting Machine. Water Resources Research. 56(8): 1-13.
Kozłowski E., Kowalska B. and Kowalski D. 2018. Water demand forecasting by trend and harmonic analysis. Archivs Civil and Mechnical.Engineering, 18: 140–148.
Meireles I., Sousa V., Bleys B. and Poncelet B. 2022. Domestic hot water consumption pattern: Relation with total water consumption and air temperature. Renewable and Sustainable Energy Reviews, 157: 112035.
Nazemi A. and Wheater H. S. 2015. On inclusion of water resource management in Earth system models–Part 1: Problem definition and representation of water demand. Hydrology and Earth System Sciences, 19(1): 33-61.
Smolak K., Kasieczka B., Fialkiewicz W., Rohm W., Siła-Nowicka K. and Kopańczyk K. 2020. Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models. Urban Water Journal, 17(1): 32-42.
Tavanpour N., Noshadi M. and Tavanpour N. 2016. Scale formation and corrosion of drinking water pipes: a case study of drinking water distribution system of Shiraz city. Modern Applied Science, 10(3): 166-177.
Wang Y., Jian L., Rong L, Xinyu S. and Enhui L.(2020. Precipitation forecast of the Wujiang River B based on artificial bee colony algorithm and backpropagation neural network, Alexandria Engineering Journal, 59: 1473–1483.
Waqas Khan P., Byun Y. C., Lee S. J. and Park N. 2020. Machine learning based hybrid system for imputation and efficient energy demand forecasting. Energies, 13(11): 2681.
Zubaidi S. L., Al-Bugharbee H., Ortega-Martorell S., Gharghan S. K., Olier I., Hashim K. S and Kot P. 2020. A novel methodology for prediction urban water demand by wavelet denoising and adaptive neuro-fuzzy inference system approach. Water, 12(6): 1628.
Zubaidi S. L., Ortega-Martorell S., Kot P., Alkhaddar R. M., Abdellatif M., Gharghan S. K and Hashim K. 2021. A method for predicting long-term municipal water demands under climate change. Water Resources Management, 34(3): 1265-1279.