IoT-Based Monitoring and Machine Learning Classification of Urban Water Consumption Patterns
Keywords:
Internet of Things, LPWAN, Machine learning classification, Smart metering, Urban water managemen, Water consumption monitoringAbstract
Efficient urban water management requires real-time monitoring and intelligent analytics to support both day-to-day operation and long-term resource optimization. This paper reports the design, deployment, and evaluation of an energy-aware LPWAN monitoring system implemented at a university facility in Mexico. Over five months, the system recorded hourly volumetric flow and transmitted a single daily burst, revealing clear circadian and weekly consumption patterns influenced by academic schedules and external events. To characterize consumption behavior, a percentile-based labeling strategy was employed to categorize daily water usage into four operational states: Low, Normal, High, and Anomalous. Using these labels, supervised machine learning models were evaluated for classification of consumption patterns, while unsupervised methods were used to explore natural groupings in the data. Among the tested models, Random Forest, Decision Tree, and XGBoost achieved
the highest classification performance under stratified 10-fold cross-validation. From an energy efficiency standpoint, once-per-day bursting delivered 3,816 hourly readings through 159 daily bursts (six 12-byte frames each; 954 frames total), reducing the number of daily transmissions by 75% relative to hourly uploads while preserving full hourly resolution. These results show that minimal sensing coupled with lightweight LPWAN telemetry can support scalable, pattern-driven water management and provide a practical foundation for context-aware decision support in smart urban distribution networks.
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