Artificial Intelligence-Based Forecasting of Water Consumption Patterns in Smart Cities

Siti Rahmawati

Artificial Intelligence-Based Forecasting of Water Consumption Patterns in Smart Cities

Author(s)	Siti Rahmawati
Country	Indonesia
Abstract	The development of smart cities has encouraged the integration of digital monitoring infrastructure, intelligent information systems, and data-driven decision-making into urban resource management. Water distribution and consumption monitoring represent important applications of these technologies because municipal water requirements vary according to population activity, weather conditions, economic characteristics, and temporal consumption patterns. Smart metering systems provide opportunities to collect detailed consumption records and investigate changes in water use across different urban service areas. However, accurate demand forecasting remains challenging because consumption behavior exhibits temporal dependencies, nonlinear relationships, irregular fluctuations, and differences among urban communities. The present paper develops a hypothetical methodological framework for investigating artificial intelligence-based forecasting of water consumption patterns in smart cities. The proposed investigation combines historical smart-meter information, meteorological indicators, calendar variables, statistical forecasting, and machine learning techniques. Five forecasting configurations are considered: Seasonal Naïve Forecasting, Seasonal Autoregressive Integrated Moving Average, Random Forest Regression, Gradient Boosting Regression, and Long Short-Term Memory networks. A fictional dataset containing 11,688 daily aggregated water consumption records from eight urban service zones across a four-year observation period is introduced to demonstrate the intended research design. The methodology incorporates data quality assessment, temporal feature engineering, seasonal consumption analysis, chronological validation, and independent forecasting evaluation. Two illustrative analytical tables present quarterly consumption patterns and comparative prediction errors among the selected methods. Mean absolute error and root mean squared error are considered primary evaluation measures. All numerical values are manually constructed and do not represent collected smart-meter records, executed forecasting experiments, or verified urban consumption patterns. The methodological discussion examines temporal variability, environmental influences, data availability, computational complexity, forecasting uncertainty, and responsible management of consumption information. Particular attention is given to distinguishing short-term demand prediction from long-term infrastructure planning. The paper concludes that artificial intelligence provides a relevant methodological foundation for forecasting urban water consumption, although reliable implementation requires representative observations, reproducible modeling, independent evaluation, and appropriate operational interpretation.
Keywords	Smart Cities, Artificial Intelligence, Water Consumption Forecasting, Smart Water Management, Machine Learning, Urban Water Demand, Smart Metering, Time-Series Forecasting, Random Forest, Long Short-Term Memory.
Field	Engineering
Published In	Volume 2, Issue 2, March-April 2026
Published On	2026-03-15

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Artificial Intelligence-Based Forecasting of Water Consumption Patterns in Smart Cities

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