Wildfire Hotspots Forecasting and Mapping for Environmental Monitoring Based on the Long Short-Term Memory Networks Deep Learning Algorithm

• Published in: Environments (Basel, Switzerland) Vol. 10; no. 7; p. 124
• Main Authors: Evizal Abdul Kadir, Hsiang Tsung Kung, Amal Abdullah AlMansour, Hitoshi Irie, Sri Listia Rosa, Shukor Sanim Mohd Fauzi
• Format: Journal Article
• Language: English
• Published: MDPI AG 01-07-2023
• Subjects: environment monitoring; Indonesia; LSTM algorithm; mapping and forecasting; wildfire hotspots
• ISSN: 2076-3298
• DOI: 10.3390/environments10070124

Global warming is raising the earth’s temperature, and resulting in increased forest fire events, especially in tropical regions with locations that are at high risk of wild and forest fires. Indonesia is a country in Southeast Asia that has experienced a severe number of wildfires, which have dangerous impacts on neighboring countries due to the emission of carbon and haze to the free air. The objective of this research is to map and plot the locations that consist of a significant number of fire hotspots and forecast the possible forest fire disasters in Indonesia based on the collected data of forest fires. The results of forecasting data are beneficial for the government and its policymakers to take preventive action and countermeasures regarding this wildfire issue. The Long Short-Term Memory (LSTM) algorithm, a deep learning method, was applied to analyze and then forecast the number of wildfire hotspots. The wildfire hotspot dataset from the year 2010 to 2022 is derived from the National Aeronautics and Space Administration’s (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS). The total number of collected observations is more than 700,000 wildfire data in Indonesia. The distribution of wildfire hotspots as shown in the results is concentrated mainly on two big islands, Kalimantan and Sumatra, Indonesia. The main issue is the peat type of land that is prone to spreading fire. Forecasting the number of hotspots for 2023 has achieved good results with an average error of 7%. Additionally, to prove that the proposed algorithm is working well, a simulation has been conducted using training data from 2018 to 2022 and testing data from 2021 to 2022. The forecasting result achieved a similar pattern of the number of fire hotspots compared to the available data in 2021 and 2022.