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Neural Networks with LSTM and GRU in Modeling Active Fires in the Amazon

Ramon Tavares, Ricardo Olinda

TL;DR

This research significantly contributes to the application of deep learning techniques in environmental monitoring, specifically in forecasting active fire spots, and highlights the potential for adaptation to other time series forecasting challenges.

Abstract

This study presents a comprehensive methodology for modeling and forecasting the historical time series of active fire spots detected by the AQUA\_M-T satellite in the Amazon, Brazil. The approach employs a mixed Recurrent Neural Network (RNN) model, combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to predict the monthly accumulations of daily detected active fire spots. Data analysis revealed a consistent seasonality over time, with annual maximum and minimum values tending to repeat at the same periods each year. The primary objective is to verify whether the forecasts capture this inherent seasonality through machine learning techniques. The methodology involved careful data preparation, model configuration, and training using cross-validation with two seeds, ensuring that the data generalizes well to both the test and validation sets for both seeds. The results indicate that the combined LSTM and GRU model delivers excellent forecasting performance, demonstrating its effectiveness in capturing complex temporal patterns and modeling the observed time series. This research significantly contributes to the application of deep learning techniques in environmental monitoring, specifically in forecasting active fire spots. The proposed approach highlights the potential for adaptation to other time series forecasting challenges, opening new opportunities for research and development in machine learning and prediction of natural phenomena. Keywords: Time Series Forecasting; Recurrent Neural Networks; Deep Learning.

Neural Networks with LSTM and GRU in Modeling Active Fires in the Amazon

TL;DR

This research significantly contributes to the application of deep learning techniques in environmental monitoring, specifically in forecasting active fire spots, and highlights the potential for adaptation to other time series forecasting challenges.

Abstract

This study presents a comprehensive methodology for modeling and forecasting the historical time series of active fire spots detected by the AQUA\_M-T satellite in the Amazon, Brazil. The approach employs a mixed Recurrent Neural Network (RNN) model, combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to predict the monthly accumulations of daily detected active fire spots. Data analysis revealed a consistent seasonality over time, with annual maximum and minimum values tending to repeat at the same periods each year. The primary objective is to verify whether the forecasts capture this inherent seasonality through machine learning techniques. The methodology involved careful data preparation, model configuration, and training using cross-validation with two seeds, ensuring that the data generalizes well to both the test and validation sets for both seeds. The results indicate that the combined LSTM and GRU model delivers excellent forecasting performance, demonstrating its effectiveness in capturing complex temporal patterns and modeling the observed time series. This research significantly contributes to the application of deep learning techniques in environmental monitoring, specifically in forecasting active fire spots. The proposed approach highlights the potential for adaptation to other time series forecasting challenges, opening new opportunities for research and development in machine learning and prediction of natural phenomena. Keywords: Time Series Forecasting; Recurrent Neural Networks; Deep Learning.
Paper Structure (20 sections, 27 equations, 19 figures, 3 tables, 1 algorithm)

This paper contains 20 sections, 27 equations, 19 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introdução
  2. Referencial Teórico
  3. Unidade RNN
  4. Unidade LSTM
  5. Unidade GRU
  6. Funções de Ativação
  7. Entendendo o Funcionamento das Camadas Densas em Redes Neurais Recorrentes
  8. Algoritmo de Otimização Adam: Uma Visão Geral
  9. Descrição dos Parâmetros
  10. Metodologia
  11. Preparação dos Dados
  12. Configuração dos Modelos
  13. Treinamento e Avaliação dos Modelos
  14. Como as Previsões são Calculadas?
  15. Resultados da Análise Estatística
  16. ...and 5 more sections

Figures (19)

  • Figure 1: Esquema detalhado da unidade de Rede Neural Recorrente (RNN) simples
  • Figure 2: Esquema detalhado da unidade LSTM
  • Figure 3: Esquema detalhado da Unidade GRU
  • Figure 4: Ilustração da transformação linear e da ativação linear em uma camada densa.
  • Figure 5: Esquema detalhado da Unidade GRU e camadas densas na saída final (previsão) em uma rede LSTM.
  • ...and 14 more figures