Paper Title
Machine Learning for Peatland Groundwater Level (GWL) Prediction Using an IoT System
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Registration ID: IJNRD_323217
Published ID: IJNRD2604343
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Keywords
—Peatland monitoring, groundwater level prediction, Internet of Things (IoT), machine learning, environmental sensors, time-series analysis, sustainable land management.
Abstract
Peatlands play a crucial role in global carbon storage, biodiversity conservation, and climate regulation. Maintaining a stable groundwater level (GWL) is essential for preserving peatland ecosystems and preventing peat degradation and fire hazards. Traditional groundwater monitoring methods rely on manual observations and limited sensing infrastructure, which often fail to provide continuous and predictive insights into groundwater dynamics. This study proposes an IoT-enabled machine learning framework for predicting peatland groundwater levels using real-time environmental sensor data. A network of IoT sensors is used to collect key environmental parameters such as groundwater level, soil moisture, rainfall, temperature, and humidity. The collected data are transmitted to a cloud platform for storage, preprocessing, and analysis. Machine learning models including Linear Regression, Random Forest, Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks are applied to analyze historical and real-time data for forecasting groundwater level variations. The proposed system enables early detection of abnormal groundwater fluctuations and supports proactive environmental management. Experimental evaluation demonstrates that the hybrid learning framework improves prediction accuracy and provides reliable early-warning indicators for drought conditions, flooding risks, and peatland fire hazards. The proposed approach contributes to sustainable peatland monitoring and data-driven environmental decision making.
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How To Cite (APA)
Dasari Varshitha, Pradeep Amgoth, Trisha Reddy Poralla, Ajay Varre, & Vijaya Babu,Prasad Bobbillapati (April-2026). Machine Learning for Peatland Groundwater Level (GWL) Prediction Using an IoT System. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(4), d503-d509. https://ijnrd.org/papers/IJNRD2604343.pdf
Issue
Volume 11 Issue 4, April-2026
Pages : d503-d509
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Paper Reg. ID: IJNRD_323217
Published Paper Id: IJNRD2604343
Research Area: Other area not in list
Author Type: Indian Author
Country: Hyderabad, Telangana, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2604343.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2604343
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