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

David Dominguez, Luis de Juan del Villar, Odette Pantoja and Mario González-Rodríguez
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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

Sustainability, 2022, vol. 14, issue 2, 1-18

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.

Keywords: deforestation; hybrid regression; dense neural network; MLP; LSTM (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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