A Probabilistic Markov Chain Model for Short-term Water Demand Forecasting

Abstract

Urban water management remains a crucial concern for city managers and planners. As water demand forecasting plays a key role in urban water management, identifying factors influencing water demand is particularly important to mitigate water shortage crises. This study utilizes a Markov chain model and Artificial Neural Networks (ANNs) to estimate short-term urban water demand in Tehran. The variables considered for estimation include maximum temperature, water consumption, and precipitation rate in the previous four days. These variables are used as previous events to predict water consumption on the fifth day. Daily data from March 21, 2018 to March 19, 2021 were collected for analysis. The results of the study indicate that the Markov model's forecasting is more accurate compared to the ANN model. The Markov chain model demonstrated 48% and 65% improvement in accuracy compared to the ANN model for the test data and the training data, respectively. This suggests that a Markov chain model can be a valuable tool for estimating short-term urban water demand. The findings of this study can contribute to better urban water management and planning to address water shortage issues effectively.