Machine Learning for Peatland Ground Water Level Prediction IoT System

Peatlands play a critical role in global carbon storage and climate regulation, making the accurate monitoring of their groundwater levels (GWL) essential for sustainable ecosystem management. This study presents an integrated Internet of Things (IoT) and Machine Learning (ML) system for real-time GWL prediction in peatland environments. The IoT component consists of low- power sensors deployed in the field to continuously collect environmental data such as temperature, humidity, rainfall, and water table depth. These data are transmitted to a cloud-based platform for storage and processing. Several ML models, including Random Forest, Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) networks, are evaluated to predict GWL based on temporal and environmental patterns. Results show that LSTM provides superior predictive performance due to its ability to model sequential dependencies in time-series data. The proposed system enables proactive peatland management by forecasting GWL fluctuations, thereby supporting early intervention strategies to prevent peat degradation, reduce fire risk, and maintain ecological balance.