Frontiers in artificial intelligence · 2026
An AI-driven hybrid random forest-LSTM framework for IoT-based groundwater level prediction and sustainable water management in Chennai, India.
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Boomika S, Sitharthan R, Tanukasree R, Senthil Kumar R
Аннотация
INTRODUCTION: Groundwater resources in Chennai, Tamil Nadu, India are under increased stress due to rapid urbanization, erratic monsoon patterns and largely unregulated groundwater extraction. Addressing this challenge requires not only continuous monitoring but also accurate forecasting of groundwater fluctuations.
METHODS: This study presents a data driven framework that combines Internet of Things (IoT) enabled sensing with Machine Learning model. Real-time measurements such as groundwater depth, rainfall, temperature, and soil moisture are combined with historical hydro-meteorological data to build a reliable prediction system. A hybrid modelling approach is adopted in this study, where Random Forest is used to identify key influencing factors and capture non-linear relationships, while Long Short-Term Memory (LSTM) networks are employed to model temporal variations.
RESULTS: The combined RF-LSTM model surpasses traditional techniques, delivering an RMSE of 0.38 m, an MAE of 0.29 m, and R2 value of 0.96. Relative to a standalone LSTM model, the hybrid approach cuts the RMSE by approximately 22%, and it also outperforms Random Forest, Support Vector Regression, and Linear Regression individually.
DISCUSSION: The model successfully tracks seasonal shifts in groundwater capturing groundwater recharge during the Northeast monsoon and depletion during the summer season. Through timely and dependable forecasts, this system aids more effective groundwater management, strengthens rainwater harvesting efforts and supports sustainable urban water practices going forward.
METHODS: This study presents a data driven framework that combines Internet of Things (IoT) enabled sensing with Machine Learning model. Real-time measurements such as groundwater depth, rainfall, temperature, and soil moisture are combined with historical hydro-meteorological data to build a reliable prediction system. A hybrid modelling approach is adopted in this study, where Random Forest is used to identify key influencing factors and capture non-linear relationships, while Long Short-Term Memory (LSTM) networks are employed to model temporal variations.
RESULTS: The combined RF-LSTM model surpasses traditional techniques, delivering an RMSE of 0.38 m, an MAE of 0.29 m, and R2 value of 0.96. Relative to a standalone LSTM model, the hybrid approach cuts the RMSE by approximately 22%, and it also outperforms Random Forest, Support Vector Regression, and Linear Regression individually.
DISCUSSION: The model successfully tracks seasonal shifts in groundwater capturing groundwater recharge during the Northeast monsoon and depletion during the summer season. Through timely and dependable forecasts, this system aids more effective groundwater management, strengthens rainwater harvesting efforts and supports sustainable urban water practices going forward.