Machine Learning para la predicción de zonas susceptibles a incendios forestales en el cantón Pindal, Ecuador - Alvarez Vega, Angel Estalin (2025)
The inappropriate use of fire has caused fires that have progressively transformed landscapes in Ecuador. In 2021, 600 forest fires were recorded, affecting 3,520.45 hectares, of which 19.07% corresponded to the province of Loja, where the Pindal canton presented 133.8 hectares burned that year, ranking among the most affected in the country. In view of this problem, this research modeled the susceptibility of forest fire occurrence in the Pindal canton. Using the Variance Inflation Factor, two input variables associated with environmental and anthropogenic factors (Moisture and NDVI) were identified. Three supervised machine learning algorithms (RL, MARS and LMT) were implemented on a dataset composed of 88 samples labeled as fire and non-fire, corresponding to the period 2018-2021. The data were divided into training (75%) and validation (25%) subsets to develop and evaluate model performance. The MARS algorithm showed the best performance, with an AUC of 0.929 in the training set and 0.893 in the validation set. With this model, a forest fire occurrence probability mosaic was generated for the period January-December 2023, in order to perform risk zoning by means of a spatio-temporal analysis. The forest fire risk zoning identified the parish of Los Órganos as a very high risk zone, especially during the dry season from August to January. Urban areas maintain a high probability of fires throughout the year due to the constant presence of anthropogenic factors. The evaluation of fire occurrence by means of machine learning algorithms turns out to be an integral method for fire management, extinction and prevention.