Implementation of IoT and Machine Learning for Monitoring and Prediction of Tank Water Levels

Abstract

The availability and quality of clean water in household storage tanks are essential yet often overlooked until problems such as depletion or contamination occur. Manual monitoring methods that rely on physical inspection tend to be inefficient, prone to delay, and unable to support predictive decision-making. This study proposes an automated monitoring solution by integrating Internet of Things (IoT) technology with Machine Learning-based analysis. The system is developed using an ESP32 microcontroller that continuously collects real-time data from an ultrasonic sensor to measure water level and a turbidity sensor to assess water clarity. The time-series data obtained is then analyzed using two algorithmic approaches. Linear Regression is employed to model the water depletion rate and generate predictions regarding the estimated remaining duration before the tank reaches an empty state. In parallel, Random Forest is applied as a comparative model to validate prediction accuracy under non-linear consumption patterns. Experimental results demonstrate that the combined IoT–Machine Learning framework provides accurate, timely, and informative insights for users. The proposed system improves water usage efficiency and strengthens early warning capabilities, making it a practical solution for supporting effective household water management.