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

Iman Salehi  Hikouei; Keith N.  Eshleman; Bambang Hero  Saharjo; Laura Graham; Grahame Applegate; Mark A Cochrane

doi:10.1016/j.scitotenv.2022.159701

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

Journal article

Peer reviewed

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

Iman Salehi Hikouei, Keith N. Eshleman, Bambang Hero Saharjo, Laura Graham, Grahame Applegate and Mark A Cochrane

Science of the Total Environment, Vol.857(Part 3), pp.1-11

2022

DOI: https://doi.org/10.1016/j.scitotenv.2022.159701

PMID: 36306856

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Abstract

Tropical peatland

Groundwater level

Extreme gradient boosting

Random forest

Wildfire

Tropical peatlands play a vital role in the global carbon cycle as large carbon reservoirs and substantial carbon sinks. Indonesia possesses the largest share (65 %) of tropical peat carbon, equal to 57.4 Gt C. Human perturbations such as extensive logging, deforestation and canalization exacerbate water losses, especially during dry seasons, when low precipitation and high evapotranspiration rates combine with the increased drainage to lower groundwater levels. Drying and increasing temperatures of the surface peat exacerbate ignition and wildfire risks within the peat soils. As such, it is critically important to know how groundwater levels in peatlands are changing over space and time. In this study, a multilinear regression model as well as two machine learning algorithms, random forest and extreme gradient boosting, were used to model groundwater level over the study period (2010−12) within a peat dome impacted by drainage canals and multiple wildfires in Central Kalimantan, Indonesia. Although all three models performed well, based on overall fit, spatial modeling of groundwater level results revealed that extreme gradient boosting (R2 = 0.998, RMSE = 0.048 m) outperformed random forest (R2 = 0.997, RMSE = 0.054 m) and multilinear regression (R2 = 0.970, RMSE = 0.221 m) near drainage canals, which are key fire ignition risk locations in the peatlands. Our study also shows that, on average, elevation and precipitation are the most important parameters influencing groundwater level spatiotemporally.

Details

Title: Using machine learning algorithms to predict groundwater levels in Indonesian tropical peatlands
Authors: Iman Salehi Hikouei (Corresponding Author) - University of Maryland Center for Environmental Science
Keith N. Eshleman (Author) - University of Maryland Center for Environmental Science
Bambang Hero Saharjo (Author) - IPB University
Laura Graham (Author) - University of the Sunshine Coast, Queensland, Tropical Forests and People Research Centre
Grahame Applegate (Author) - University of the Sunshine Coast, Queensland, Tropical Forests and People Research Centre
Mark A Cochrane (Author) - University of Maryland Center for Environmental Science
Publication details: Science of the Total Environment, Vol.857(Part 3), pp.1-11
Publisher: Elsevier BV
Date published: 2022
DOI: 10.1016/j.scitotenv.2022.159701
ISSN: 1879-1026
PMID: 36306856
Organisation Unit: Tropical Forests and People Research Centre; University of the Sunshine Coast, Queensland; Forest Industries Research Centre; Forest Research Institute
Language: English
Record Identifier: 99681395002621
Output Type: Journal article

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