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This study focuses on predicting the impact of climate change on natural forest reserves in Burkina Faso, using deep learning techniques, with a specific case study on Arli National Park. The paper serves as a comprehensive guide for utilizing deep learning, specifically long short-term memory (LSTM) and recurrent neural network (RNN), in environmental conservation. It outlines the process of data collection, preprocessing, and model development, using climate data spanning from 1980 to 2022, obtained from NASA. The paper showcases how LSTM can be applied to improve environmental conservation efforts by forecasting climatic changes based on time series patterns of parameters like temperature, precipitation, relative humidity, and more. The research emphasizes the importance of choosing appropriate evaluation metrics for deep learning models and highlights LSTM’s superior performance, encouraging researchers to explore diverse models for real-world problem-solving. Ultimately, this paper offers a valuable resource for environmental researchers, practitioners, and students seeking to apply deep learning in environmental conservation, providing a case study and algorithmic guidance.

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Predicting Precipitation Time Series Dynamics in West Africa Using Deep Learning Models

  • 章节

  • 发表日期：

    2025年

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  • Tolulope Adedoyin Oladeji,
  • Barnabas Timilehin Adeyemo,
  • O. Olawale Awe

摘要

This study focuses on predicting the impact of climate change on natural forest reserves in Burkina Faso, using deep learning techniques, with a specific case study on Arli National Park. The paper serves as a comprehensive guide for utilizing deep learning, specifically long short-term memory (LSTM) and recurrent neural network (RNN), in environmental conservation. It outlines the process of data collection, preprocessing, and model development, using climate data spanning from 1980 to 2022, obtained from NASA. The paper showcases how LSTM can be applied to improve environmental conservation efforts by forecasting climatic changes based on time series patterns of parameters like temperature, precipitation, relative humidity, and more. The research emphasizes the importance of choosing appropriate evaluation metrics for deep learning models and highlights LSTM’s superior performance, encouraging researchers to explore diverse models for real-world problem-solving. Ultimately, this paper offers a valuable resource for environmental researchers, practitioners, and students seeking to apply deep learning in environmental conservation, providing a case study and algorithmic guidance.