Genetic Programming Method in Urban Water Consumption Prediction (Case Study: Najafabad City)

فصلنامه آب و توسعه پایدار

Article 8, Volume 10, Issue 3 - Serial Number 29, December 2023, Pages 87-98   

Document Type: Applied Article

DOI: 10.22067/jwsd.v10i3.2306-1251

Authors

M. R. Alikhani¹; R. Moeini^{* 2}

¹M.Sc. Student , Department of Civil Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran

²Associate Professor, Department of Civil Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran

Abstract

Improving the lifestyle of city residents is conditional on benefiting from high-quality urban infrastructure to satisfy daily demands. The urban water supply network is one of the most basic urban infrastructures, and its optimal design and service are essential during the planning period. Therefore, it is important to determine the actual amount of consumption and predict it for the future. For this purpose, in this research, a method based on artificial intelligence, i.e., genetic programming (GP), as well as Pearson's correlation coefficient data mining method, is proposed. The data mining method is applied here for the database, including daily data on temperature, precipitation, humidity, and the amount of daily water produced in Najafabad city (presenting the total water consumption) from the beginning of 2014 to the end of 2018, and the best set of input data vectors is selected. The selected data are used as input data vectors for the proposed. The obtained results are compared with the results of models based on artificial neural network (ANN). To investigate the performance of the models, R², RMSE, and NSE statistical indices are calculated. A comparison of the results indicates the acceptable performance of the proposed models based on the GP. In other words, the values of RMSE, NSE, R², and MAPE statistical indices for training data in the best GP model are equal to 3262.59 MCM, 0.80, 0.80, and 5.38%, respectively, and for test data equal to 3507.68 MCM, 0.78, 0.78, and 6.67%.

Keywords

Water Consumption; Prediction; Artificial Intelligence; Genetic Programming; Artificial Neural Network; Najafabad City

References

Barati, R., Salehi Neyshabouri, S. A. A., & Ahmadi, G. (2014). Development of empirical models with high accuracy for estimation of drag coefficient of flow around a smooth sphere: An evolutionary approach. Powder Technology, 257, 11-19. doi: 10.1016/j.powtec.2014.02.045
Brentan, B. M., Luvizotto Jr, E., Herrera, M., Izquierdo, J., & Pérez-García, R. (2017). Hybrid regression model for near real-time urban water demand forecasting. Journal of Computational and Applied Mathematics, 309, 532-541. doi: 10.1016/j.cam.2016.02.009
Goodarzi, M., Abedi-Koupai, J., Heidarpour, M., & Safavi, H. R. (2016). Evaluation of the Effects of Climate Change on Groundwater Recharge Using a Hybrid Method. Water Resources Management, 30(1), 133-148. doi: 10.1007/s11269-015-1150-4
Kazemi, M., & Barati, R. (2022). Application of dimensional analysis and multi-gene genetic programming to predict the performance of tunnel boring machines. Applied Soft Computing, 124, 108997. doi: 10.1016/j.asoc.2022.108997
Kühnert, C., Gonuguntla, N. M., Krieg, H., Nowak, D., & Thomas, J. A. (2021). Application of LSTM Networks for Water Demand Prediction in Optimal Pump Control. Water, 13(5), 644. doi: 10.3390/w13050644
Lee, M., Tansel, B., & Balbin, M. (2011). Influence of residential water use efficiency measures on household water demand: A four year longitudinal study. Resources, Conservation and Recycling, 56(1), 1-6. doi: 10.1016/j.resconrec.2011.08.006
Pacchin, E., Gagliardi, F., Alvisi, S., & Franchini, M. (2019). A Comparison of Short-Term Water Demand Forecasting Models. Water Resources Management, 33(4), 1481-1497. doi: 10.1007/s11269-019-02213-y
Polebitski Austin, S., & Palmer Richard, N. (2010). Seasonal Residential Water Demand Forecasting for Census Tracts. Journal of Water Resources Planning and Management, 136(1), 27-36. doi: 10.1061/(ASCE)WR.1943-5452.0000003
Romano, G., Salvati, N., & Guerrini, A. (2014). Estimating the Determinants of Residential Water Demand in Italy. Water, 6, 2929–2945. doi: 10.3390/w6102929
Sebri, M. (2016). Forecasting urban water demand: A meta-regression analysis. Journal of Environmental Management, 183, 777-785. doi: 10.1016/j.jenvman.2016.09.032
Shabani, S., Candelieri, A., Archetti, F., & Naser, G. (2018). Gene expression programming coupled with unsupervised learning: a two-stage learning process in multi-scale, short-term water demand forecasts. Water, 10(2), 142. doi: 10.3390/w10020142
Shuang, Q., & Zhao, R. T. (2021). Water Demand Prediction Using Machine Learning Methods: A Case Study of the Beijing–Tianjin–Hebei Region in China. Water, 13(3), 310. doi: 10.3390/w13030310
Silva, S., & Almeida, J. (2003). Gplab–a genetic programming toolbox for matlab. In: Proceedings of the Nordic MATLAB Conference. Copenhagen, Denmark.
Smolak, K., Kasieczka, B., Fialkiewicz, W., Rohm, W., Siła-Nowicka, K., & 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. doi: 10.1080/1573062X.2020.1734947
Suero Francisco, J., Mayer Peter, W., & Rosenberg David, E. (2012). Estimating and Verifying United States Households’ Potential to Conserve Water. Journal of Water Resources Planning and Management, 138(3), 299-306. doi: 10.1061/(ASCE)WR.1943-5452.0000182
Xenochristou, M., & Kapelan, Z. (2020). An ensemble stacked model with bias correction for improved water demand forecasting. Urban Water Journal, 17(3), 212-223. doi: 10.1080/1573062X.2020.1758164