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

doi:10.3390/su14020691

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

2022-01-09

5

OA

AI

D

David Domínguez

L

Luis de Juan del Villar

DOI：10.3390/su14020691

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Abstract

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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

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

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