IoT-Enabled Machine Learning Framework for Ground Water Level Prediction in Peatland Ecosystems

Peatland ecosystems are important for biodiversity and carbon storage, but they are vulnerable to fluctuations in the water table leading to improper environmental health and agricultural productivity. Accurate and timely Ground Water Level (GWL) predictions are necessary to overcome these challenges. The research presents an IoT-based machine learning-enabled framework for predicting the GWL, using real-time IoT sensor data on soil moisture, temperature, and atmospheric pressure. The system uses sophisticated algorithms in its analysis of data to predict GWL with great accuracy using Random Forest and LSTM. Thus, the system ensures timely alerts for stakeholders to manage peatland water resources effectively by being cost-effective and scalable. The real-time monitoring and predictive insights offered may also allow it to eventually integrate with the irrigation system for proactive intervention. All these approaches aim to enhance peatland management, reduce environmental degradation, and support sustainable agricultural practices with incentives for preserving ecological stability and economic resilience.