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Using machine learning algorithms to predict groundwater levels in Indonesian tropical peatlands.

Iman Salehi Hikouei¹, Keith N Eshleman¹, Bambang Hero Saharjo²

¹Appalachian Laboratory, University of Maryland Center for Environmental Science, Frostburg, MD, USA.

The Science of the Total Environment

|October 28, 2022

Summary

Accurate groundwater level monitoring in Indonesian tropical peatlands is crucial for carbon cycle management. Extreme gradient boosting models peatland water levels effectively, outperforming other methods near drainage canals, vital for wildfire prevention.

Keywords:

Extreme gradient boosting Groundwater level Random forest Tropical peatland Wildfire

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Area of Science:

Environmental Science
Hydrology
Ecology

Background:

Tropical peatlands are critical global carbon reservoirs and sinks, with Indonesia holding the largest share.
Human activities like logging and canalization lower peatland groundwater levels, increasing wildfire risk, especially during dry seasons.
Understanding spatiotemporal groundwater level changes is vital for peatland management and carbon cycle integrity.

Purpose of the Study:

To model and analyze groundwater level dynamics in Indonesian tropical peatlands.
To compare the performance of machine learning algorithms against traditional statistical models for groundwater level prediction.
To identify key environmental drivers influencing groundwater levels in peatland ecosystems.

Main Methods:

Utilized multilinear regression, random forest, and extreme gradient boosting models to simulate groundwater levels.
Conducted spatial modeling of groundwater levels within a Central Kalimantan peat dome (2010-2012).
Assessed model performance using R-squared and Root Mean Square Error (RMSE) metrics.

Main Results:

All models demonstrated strong performance, with extreme gradient boosting achieving the highest accuracy (R² = 0.998, RMSE = 0.048 m).
Extreme gradient boosting significantly outperformed random forest and multilinear regression in predicting groundwater levels near drainage canals.
Elevation and precipitation were identified as the most influential factors affecting groundwater levels across space and time.

Conclusions:

Machine learning, particularly extreme gradient boosting, offers a highly effective approach for modeling tropical peatland groundwater levels.
Accurate groundwater level prediction near drainage canals is essential for mitigating wildfire ignition risks in these vital ecosystems.
Spatio-temporal analysis of groundwater dynamics, considering factors like elevation and precipitation, is key to sustainable peatland management and carbon storage.