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Forecasting Water Demand, Distribution Efficiency and Consumption Patterns in Sokoto Metropolis Using a Hybrid ARIMA-ANN Model

Attahiru Hussaina Wala, Yakubu Musa, Yahaya Kabiru

Abstract

Water scarcity in rapidly urbanizing semi-arid regions necessitates precise forecasting for sustainable management. This study develops and applies a novel hybrid Autoregressive Integrated Moving Average-Artificial Neural Network (ARIMA-ANN) model to forecast water demand, distribution efficiency, and consumption patterns in Sokoto Metropolis, Nigeria. Utilizing monthly data (2018–2024) from the Sokoto State Ministry of Water Resources, the research compares the performance of standalone ARIMA, standalone ANN, and the proposed hybrid model. Evaluation metrics Root Mean Squared Error , Mean Absolute Error , and Mean Absolute Percentage Error consistently demonstrated the hybrid model's superiority. The hybrid model achieved a MAPE of 2.57% for water demand, 1.65% for distribution efficiency, and 1.57% for consumption rate, outperforming both individual models. Forecasts for 2025–2026 indicate a steady rise in water demand from 335.23 to 404.93 million litres, while distribution efficiency (78.40%) and consumption rate (83.48%) remain stagnant, highlighting critical inefficiencies. The study concludes that the hybrid ARIMA-ANN framework provides a robust, accurate tool for urban water resource planning, offering actionable insights for infrastructure investment and policy formulation in water-stressed cities.

Keywords

Water Demand ForecastingHybrid ARIMA-ANNSokoto MetropolisDistribution EfficiencyUrban Water ManagementTime Series Analysis

References

[1] Gleick, P. H. The World’s Water Volume 8: The Biennial Report on Freshwater Resources. Island Press, 2014. [2] Connor, R. The United Nations World Water Development Report 2015: Water for a Sustainable World, vol. 1. UNESCO Publishing, 2015. [3] Mays, L. W. Water Distribution Systems Handbook. McGraw-Hill, 2000. [4] Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. Time Series Analysis: Forecasting and Control, 5th ed. Wiley, 2015. [5] Kisi, O., Shiri, J., & Tombul, M. Modeling rainfall-runoff process using soft computing techniques. Computers & Geosciences, vol. 51, pp. 108–117, 2013. [6] Haykin, S. Neural Networks and Learning Machines, 3rd ed. Pearson Education, 2009. [7] Awad, M., & Zaid-Alkelani, M. Prediction of water demand using artificial neural networks models and statistical model. International Journal of Intelligent Systems and Applications, vol. 11, no. 9, pp. 40–49, 2019. [8] Zhang, G. P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, vol. 50, pp. 159–175, 2003. [9] Musa, Y., & Joshua, S. Analysis of ARIMA-artificial neural network hybrid model in forecasting of stock market returns. Asian Journal of Probability and Statistics, vol. 6, no. 2, pp. 42–53, 2020. [10] Ahmad, S., Hafusat, T., Zayyan, A. S., & Dadangarba, A. Statistical analysis of domestic water demand and supply for Kaduna North, Kaduna State. European Journal of Engineering and Technology Research, vol. 8, no. 1, pp. 26–31, 2023. [11] Dickey, D. A., & Fuller, W. A. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, vol. 74, no. 366a, pp. 427–431, 1979. [12] Bishop, C. M., & Nasrabadi, N. M. Pattern Recognition and Machine Learning. Springer, 2006.