Environmental drivers and spatial prediction of forest fires in the Western Ghats biodiversity hotspot, India: An ensemble machine learning approach

In ecologically sensitive areas like the Western Ghats, fire is largely considered to be the most significant management concern. Therefore, identifying and predicting the fire susceptible areas is crucial for managing protected area networks. In this study, we investigated the factors affecting forest fires and modelled the fire susceptibility in a human-dominated landscape in the central Western Ghats, India, by employing remote sensing, geographic information systems and machine learning techniques. We used six machine learning models (MLMs), including artificial neural network, random forest, generalized linear model, maximum entropy, multivariate adaptive regression splines, gradient boosting machine and the ensemble model, to relate the occurrence data of fires to 14 predictor variables categorized as climate, topography, vegetation, and anthropogenic disturbances to generate fire susceptibility maps. The area under the receiver operating characteristic (AUC-ROC), true skill statistic (TSS) curves, accuracy and continuous Boyce index (CBI) were used to assess the model's accuracy. We found that all models achieved acceptable performance while validating the independent test data. However, ensemble model had the best overall performance with an AUC-ROC = 0.93; TSS = 0.72; ACCURACY = 0.89; and CBI = 0.95. The land-use and land-cover, and the distance to the agricultural fields and settlements were the key determinants of fire occurrences in the study area based on the ensemble model. The results showed that the landscape has a high to very high fire risk for about 26.5% of the total area. Forest fires have mostly occurred in the northeastern regions of the landscape, which are dominated by deciduous forests and plantations, whereas western regions are less susceptible to fires. Finally, we developed an ensemble-based fire susceptibility map with a resolution of 10 m that may be used as a tool for the prevention of forest fires. Further, it aids the relevant authorities in mitigating the disastrous effects and safeguarding the environment. The results demonstrated that ensemble-based MLMs can be employed effectively for fire prediction in other regions.