IOT - Enabled Machine learning for Ground Water Level monitoring in peatlands

Peatlands are a critical ecological concern due to their susceptibility to extensive carbon emissions during wildfires. Traditional methods for monitoring Ground Water Level (GWL) in these areas are labor-intensive, lack real-time insights, and impede proactive fire management. This study introduces an Internet of Things (IoT)-based system integrated with a neural network model for real-time GWL prediction. The proposed approach leverages atmospheric parameters to forecast GWL, allowing stakeholders to implement timely preventive measures to mitigate fire hazards. The neural network model exhibits high predictive accuracy, achieving a Root Mean Square Error (RMSE) ranging from 3.554 to 4.920. This ensures a 99% accuracy level within a deviation of 14.760 mm from actual GWL measurements. The study highlights the effectiveness of IoT-based solutions in overcoming the limitations of conventional GWL monitoring. By integrating neural networks with real-time data acquisition, the proposed framework offers a novel method for predicting GWL in resource-constrained regions.