Forest fire risk mapping using machine learning technique based on GIS and remote sensing data, a case study in Dak Lak province, Vietnam

Abstract

Vietnam, a tropical nation, is frequently exposed to the risk of forest fires, driven by a combination of extreme weather events and anthropogenic activities. Forest fires present significant threats to ecosystems, human life, and infrastructure. Early detection and forecasting of forest fire risks play a pivotal role in mitigating the impact and facilitating effective responses to such disasters. This study investigates the application of advanced machine learning models, specifically Random Forest (RF), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), in conjunction with geospatial data, to construct a forest fire risk prediction map for Dak Lak province in the Central Highlands of Vietnam. The input dataset comprises 12 variables: vegetation cover, surface temperature, average monthly precipitation, wind speed, evapotranspiration, population density, land use, elevation, slope, aspect, proximity to roads, and proximity to hydrological features. These data were derived using remote sensing and GIS technologies, and integrated with historical fire occurrence points (6,088 fire locations) to model fire risk within the study area. The prediction performance of the models was evaluated using several metrics, including Precision, Recall, F1-score, Accuracy, and AUC, with a test dataset consisting of 122 fire points and 170 non-fire points. The results demonstrate that the RF algorithm (Precision: 0.84, Accuracy: 0.88, AUC: 0.95) outperforms both CNN (Precision: 0.80, Accuracy: 0.80, AUC: 0.84) and LSTM (Precision: 0.81, Accuracy: 0.83, AUC: 0.89) models in terms of forecasting forest fire risks in Dak Lak province.. The findings underscore the potential of leveraging advanced machine learning techniques and spatial data analysis to enhance the accuracy of forest fire risk prediction. This study’s outcomes are expected to assist policymakers and forest management authorities in developing more effective forest management strategies and fire prevention measures, thereby contributing to the protection of natural resources and the welfare of local communities.