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Forecasting Amazon Rain-Forest Deforestation Using a Hybrid Machine Learning Model

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  • David Dominguez

    (Grupo de Neurocomputación Biólogica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain)

  • Luis de Juan del Villar

    (Grupo de Neurocomputación Biólogica, Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad Autónoma de Madrid, 28049 Madrid, Spain)

  • Odette Pantoja

    (Grupo de Investigación Multidisciplinar en Sistemas de Información, Gestión de la Tecnonlogía e Innovación, Escuela Politécnica Nacional, Quito 170525, Ecuador)

  • Mario González-Rodríguez

    (SI2Lab, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de las Américas, Quito 170124, Ecuador)

Abstract

The present work aims to carry out an analysis of the Amazon rain-forest deforestation, which can be analyzed from actual data and predicted by means of artificial intelligence algorithms. A hybrid machine learning model was implemented, using a dataset consisting of 760 Brazilian Amazon municipalities, with static data, namely geographical, forest, and watershed, among others, together with a time series data of annual deforestation area for the last 20 years (1999–2019). The designed learning model combines dense neural networks for the static variables and a recurrent Long Short Term Memory neural network for the temporal data. Many iterations were performed on augmented data, testing different configurations of the regression model, for adjusting the model hyper-parameters, and generating a battery of tests to obtain the optimal model, achieving a R-squared score of 87.82%. The final regression model predicts the increase in annual deforestation area (square kilometers), for a decade, from 2020 to 2030, predicting that deforestation will reach 1 million square kilometers by 2030, accounting for around 15% compared with the present 1%, of the between 5.5 and 6.7 millions of square kilometers of the rain-forest. The obtained results will help to understand the impact of man’s footprint on the Amazon rain-forest.

Suggested Citation

  • David Dominguez & Luis de Juan del Villar & Odette Pantoja & Mario González-Rodríguez, 2022. "Forecasting Amazon Rain-Forest Deforestation Using a Hybrid Machine Learning Model," Sustainability, MDPI, vol. 14(2), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:2:p:691-:d:720680
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    References listed on IDEAS

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    1. Miyamoto, Motoe, 2020. "Poverty reduction saves forests sustainably: Lessons for deforestation policies," World Development, Elsevier, vol. 127(C).
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