Vol. 4 No. 4 (2025): October
RESEARCH ARTICLES

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

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  • P Parimala Kumari,
  • P Vidhura,
  • A Eshwari,
  • K Jaya Shankar,
  • G V Krishna Mohan,
  • V Vinod Kumar Reddy
P Parimala Kumari
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
P Vidhura
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
A Eshwari
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
K Jaya Shankar
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
G V Krishna Mohan
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
V Vinod Kumar Reddy
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India
DOI: https://doi.org/10.5281/zenodo.15251503

Published 2025-04-20

Keywords

  • Wetland Hydrology Forecasting,
  • Remote Sensing for Subsurface Moisture,
  • AI-Enhanced Ecosystem Stability,
  • Distributed Sensor Networks

How to Cite

P Parimala Kumari, P Vidhura, A Eshwari, K Jaya Shankar, G V Krishna Mohan, & V Vinod Kumar Reddy. (2025). IoT - Enabled Machine learning for Ground Water Level monitoring in peatlands. International Journal of Computational Learning & Intelligence, 4(4), 757–765. https://doi.org/10.5281/zenodo.15251503

Copyright (c) 2025 P Parimala Kumari, P Vidhura, A Eshwari, K Jaya Shankar, G V Krishna Mohan, V Vinod Kumar Reddy

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

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.

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