Enhancing Water Management in Buildings Through IoT-Based Monitoring and Machine Learning Powered Analytics

Abstract

In both agricultural and residential buildings, effective water management is crucial. However, most of the existing literature has focused on Internet of Things (IoT)-based solutions for water management (e.g., turning water pumps on and off), but these approaches lack the ability to analyze water usage patterns or predict future consumption. This paper addresses this limitation by developing an automatic water level monitoring and control system using IoT and Machine Learning (ML) techniques. Specifically, an IoT circuit comprising various sensors, an ESP32 microcontroller, and related components was designed to collect real-time water usage data. The collected data was then preprocessed and analyzed using ML techniques such as Long Short-Term Memory (LSTM) networks for time-series prediction of water flow rates and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for anomaly detection in sensor data (including level, flow, pH, and turbidity). In summary, this work not only automates water level monitoring and control through IoT but also improves water management by applying ML techniques to predict future usage patterns and detect anomalies in real-time sensor data