Machine Learning Framework for Urban Domestic Water Demand Forecasting: A Case Study of the Aththidiya South GN Division

Abstract

This study examines domestic water demand forecasting in the Aththidiya South Grama Niladhari Division, Colombo, Sri Lanka, where urbanization, climate variability, and ageing infrastructure are straining water supply systems. Accurate demand prediction is critical for effective planning and resource management. Traditional methods such as linear regression and ARIMA are limited by assumptions of linearity and stationarity, restricting their ability to capture the complex, dynamic behaviour of urban water consumption. To address this, the study develops a two-stage forecasting framework combining machine learning with classical time-series modelling. First, a Random Forest regression model identifies and ranks key predictors of domestic water demand using socio-economic, climatic, and infrastructure-related variables. Second, a Long Short-Term Memory (LSTM) neural network forecasts monthly water demand, capturing temporal dependencies and non-linear consumption patterns. ARIMA(1,1,1) and SARIMA(1,1,1)(1,1,1,7) models were developed for benchmarking. Data were sourced from the National Water Supply and Drainage Board, the Department of Meteorology, and a household survey conducted in the study area. Results show monthly household water consumption ranging from 3–37 m3, averaging 11.3 m3. Household size was the most influential predictor (52% relative importance), followed by rainfall (25%) and temperature (22%). The LSTM model outperformed classical benchmarks, achieving an RMSE of 0.78 m3 and MAPE of 21.32% on the test dataset. As a case study, the framework offers promising practical value for water resource planning in the study area, though the limited sample and single-division scope mean that broader generalization will require validation across additional GN divisions and larger datasets.