Predicting Groundwater Levels Using Borehole Data for Sustainable Management Systems

Abstract Groundwater depletion presents a major obstacle to achieving sustainable agriculture, especially in areas such as Karnataka, India. Conventional groundwater monitoring techniques tend to be manual, sporadic, and do not support real-time data integration, which often results in inefficient management of water resources. This project introduces an affordable IoT-based system designed for continuous monitoring and management of groundwater levels. By leveraging ESP8266 microcontrollers, the system combines soil moisture sensors with ultrasonic water level sensors to gather up-to-date information on soil moisture and water availability. The data is transmitted over Wi-Fi to the Blynk IoT cloud platform, enabling users to remotely monitor and control the system through a user-friendly mobile application. Furthermore, the integration of weather information via APIs supports predictive analytics, assisting in more informed irrigation and water management decisions. The automation of pump control based on sensor readings and weather forecasts helps maximize water efficiency, reduce over-extraction, and support sustainable groundwater practices. This solution is designed to be scalable and easily accessible, providing farmers and stakeholders with an effective tool to improve the sustainability of water resources in agriculture.