Smart Water Flow Monitoring and Forecasting System using IoT and Machine Learning

This paper presents the design and implementation of a smart water flow measurement and forecasting system for homes and offices. The system utilises a NodeMCU microcontroller, a water flow sensor, a 16x2 I2C LCD, and an 8-bit NeoPixel LED, all housed within a 3D-printed enclosure. Real-time water flow data is collected by the sensor and displayed on both the LCD and a user-friendly dashboard created using the Arduino IoT Cloud platform. The NeoPixel LED provides a visual indication of daily water consumption, changing colour based on usage levels. An integrated buzzer activates when consumption exceeds 200 litres, accompanied by a red NeoPixel alert. Data is also seamlessly logged to a Google Spreadsheet for further analysis. Furthermore, the system employs machine learning algorithms, including random forest and linear regression, to forecast future water usage patterns. The device is powered by a rechargeable 3.3V LiPO battery with fast charging capabilities, ensuring continuous operation. This integrated approach provides users with real-time insights into their water consumption, promotes water conservation, and enables proactive management of water resources.