Design of a Predictive Model Using AI and GIS for the Management and Optimization of Resources in Forest Fires in Eastern Michoacán

Authors

DOI:

https://doi.org/10.61467/2007.1558.2026.v17i1.1181

Keywords:

Forest fires, predictive models, multilayer perceptron, geographic information systems

Abstract

This study describes a design and validation proposal for an artificial intelligence–based predictive model to estimate the area affected by forest fires and support the optimisation of operational resource use in the eastern region of Michoacán, Mexico. Using a dataset comprising 930 historical records (2015–2024), three supervised learning algorithms were evaluated: Multilayer Perceptron (MLP), Random Forest (RF), and XGBoost. The MLP model, optimised using L2 regularisation, Dropout, and cross-validation, achieved the highest performance, with a coefficient of determination (R²) of 0.76, compared with RF (0.57), XGBoost (0.51), and a weighted average ensemble model (0.60). The results were integrated into interactive cartographic platforms using QGIS and Leaflet JS, enabling the visualisation of areas with higher fire susceptibility and the prioritisation of interventions. A replicable tool with low computational requirements is put forward as suitable for institutional contexts with limited resources.

 

Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1181
Dimensions.
Open Alex.

References

Alkhatib, R., Sahwan, W., Alkhatieb, A., & Schütt, B. (2023). A brief review of machine learning algorithms in forest fires science. Applied Sciences, 13(14), 8275. https://doi.org/10.3390/app13148275

Andrianarivony, H. S., & Akhloufi, M. A. (2024). Machine learning and deep learning for wildfire spread prediction: A review. Fire, 7(12), 482. https://doi.org/10.3390/fire7120482

Bassaber Zuñiga, A. I., Maldonado Villegas, M. M., Curiel Guevara, S., Rojas Arias, M. L., & Valdez Rosas, J. S. (2024). Revisión de causas, consecuencias y medidas de respuesta frente a los incendios forestales: Un enfoque en el estado de Jalisco. Revista Científica e-CUCBA, 21, 100–114. https://doi.org/10.32870/e-cucba.vi21.327

Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO). (n.d.). Áreas protegidas. Retrieved March 15, 2025, from https://www.biodiversidad.gob.mx/region/areasprot

CONAFOR. (2024). Reporte semanal de incendios. Comisión Nacional Forestal. https://www.gob.mx/conafor/documentos/reporte-semanal-de-incendios

Cruz Núñez, X., & Bulnes Aquino, E. (2019). Emission impact of wildfires: El Tepozteco 2016. Atmósfera, 32(2), 85–93. https://doi.org/10.20937/ATM.2019.32.02.01

España Boquera, M., Champo Jiménez, O., & Uribe Salas, M. (2024). Extensión y severidad de incendios forestales en Michoacán en 2021 a partir de imágenes Sentinel-2. Polibotánica, 57, 125–144. https://doi.org/10.18387/polibotanica.57.7

Flores Garnica, J. G. (2021). Antecedentes y perspectivas de la investigación en incendios forestales en el INIFAP. Revista Mexicana de Ciencias Forestales, 12(Especial), e981. https://doi.org/10.29298/rmcf.v12iEspecial-1.981

González Gutiérrez, I., Farfán Gutiérrez, M., Morales Manilla, L. M., Pérez Salicrup, D. R., Garza Saldaña, J. J., & Medina Puente, A. (2023). Índices y modelos para la predicción de la ocurrencia de incendios forestales: Una revisión para México. Revista Geográfica de América Central, 71(2), 193–219. https://doi.org/10.15359/rgac.71-2.7

González R., A., & Ortiz P., C. F. (2022). Superficie forestal afectada por incendios en México: Apuntes iniciales hacia un modelo de manejo preventivo. Revista de Ciencias Ambientales, 56(1), 1–27. https://doi.org/10.15359/rca.56-1.1

Huerta Silva, M. H., Parra Vázquez, M. R., & Ianni, E. (2024). Relación entre pobreza y conservación del capital natural desde los medios de vida sustentables en Villaflores, Chiapas. Revista de Ciencias Ambientales, 58(1), 19352. https://doi.org/10.15359/rca.58-1.9

Ibarra Montoya, J. L., & Huerta Martínez, F. M. (2016). Cambio climático y predicción de incendios al 2050 en el Bosque La Primavera, Jalisco. Revista Mexicana de Ciencias Forestales, 7(37), 39–50. https://doi.org/10.29298/rmcf.v7i37.50

Latorre-Cárdenas, M. C., González-Rodríguez, A., Godínez-Gómez, O., Arima, E. Y., Young, K. R., Denvir, A., … Ghilardi, A. (2023). Estimación de patrones de fragmentación y conectividad del bosque templado en un paisaje dominado por aguacates. Tierra, 12(3), 631. https://www.redalyc.org/journal/2818/281857987010/

Martínez Alcántar, J. L., Ruiz Garduño, J. K., Mora Patiño, G., & Viñas Álvarez, S. E. (2025). Modelo predictivo para la gestión de incendios forestales en Michoacán con redes neuronales [Unpublishing]. 2° Congreso Nacional de Ingeniería Industrial y Sistemas.

Martínez Saucedo, A., & Ezequiel Inchausti, P. (2023). Predicción de incendios forestales mediante modelos de machine learning. En Actas del XXVIII Congreso Argentino de Ciencias de la Computación (CACIC). http://sedici.unlp.edu.ar/handle/10915/149568

Moghim, S., & Mehrabi, M. (2024). Wildfire assessment using machine learning algorithms in different regions. Fire Ecology, 20, 104. https://doi.org/10.1186/s42408-024-00335-2

Neger, C., León Cruz, J. F., Galicia Sarmiento, L., & Manzo Delgado, L. D. (2022). Dinámica espaciotemporal, causas y efectos de los megaincendios forestales en México. Madera y Bosques, 28(2), e2822453. https://doi.org/10.21829/myb.2022.2822453

Pati, A., Rout, R. R., & Tripathy, R. K. (2024). Spatial analysis and machine learning prediction of forest fire susceptibility: A comprehensive approach for effective management and mitigation. Science of the Total Environment, 926, 170048. https://doi.org/10.1016/j.scitotenv.2024.170048

Ribeiro, L., Gama, J., & Gonçalves, R. (2018). Fireman: A GIS-based decision support system for wildland fire management. International Journal of Geographical Information Science, 32(11), 2191–2213. https://doi.org/10.1080/13658816.2018.1472396

Román Cuesta, R. M., & Martínez Vilalta, J. (2006). Effectiveness of protected areas in mitigating fire: Case study of Chiapas. Conservation Biology, 20(4), 1074–1086. https://doi.org/10.1111/j.1523-1739.2006.00478.x

Santelices Moya, R., Cabrera Ariza, A., Silva Flores, P., & Navarro Cerrillo, R. M. (2022). Assessment of a wildfire in Nothofagus alessandrii forests based on Sentinel-2. International Journal of Agriculture and Natural Resources, 49(2), 85–96. https://doi.org/10.7764/ijanr.v49i2.2337

Ye, J., Wang, N., Sun, M., Liu, Q., Ding, N., & Li, M. (2023). A new method for rapid detection of forest fire disturbance events using GEE and the VCT algorithm: A case study in China. Remote Sensing, 15(2), 413. https://doi.org/10.3390/rs15020413

Downloads

Published

2026-01-02

How to Cite

Viñas Alvarez, S. E., González Ramírez, C. T., Martínez Alcantar, J. L., & Mora Patiño, G. (2026). Design of a Predictive Model Using AI and GIS for the Management and Optimization of Resources in Forest Fires in Eastern Michoacán. International Journal of Combinatorial Optimization Problems and Informatics, 17(1), 378–385. https://doi.org/10.61467/2007.1558.2026.v17i1.1181

Issue

Vol. 17 No. 1 (2026)

Section

CNIIS 2025

License

Copyright (c) 2025 International Journal of Combinatorial Optimization Problems and Informatics

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Most read articles by the same author(s)