Mapping fire hazard potential in Kazakhstan: a machine learning and remote sensing perspective

Daniker Chepashev; Serik Nurakynov; Divyansh Sharma; Nurmakhambet Sydyk; Gulzhiyan Kabdulova

doi:10.1071/WF24232

RESEARCH ARTICLE (Open Access)

Previous Contents Vol 34(9)

Mapping fire hazard potential in Kazakhstan: a machine learning and remote sensing perspective

Daniker Chepashev ^A , Serik Nurakynov

^A ^* , Divyansh Sharma ^B , Nurmakhambet Sydyk ^A and Gulzhiyan Kabdulova ^A

+ Author Affiliations

- Author Affiliations

^A Laboratory of Space Monitoring of Emergencies, Institute of Ionosphere, Almaty 050000, Kazakhstan.

^B Department of Sustainable Engineering, TERI School of Advanced Studies, New Delhi 110 070, India.

^* Correspondence to: snurakynov@ionos.kz

International Journal of Wildland Fire 34, WF24232 https://doi.org/10.1071/WF24232

Submitted: 2 January 2025 Accepted: 22 July 2025 Published: 20 August 2025

© 2025 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Background

Wildfires pose significant environmental challenges in Kazakhstan, exacerbated by climate change and human activities. With vast landscapes ranging from grasslands to dense forests, the country is highly vulnerable to wildfires, yet lacks comprehensive predictive tools for fire risk assessment.

Aims

This study aims to develop a wildfire hazard map for Kazakhstan using the Maximum Entropy (MaxEnt) model, integrating environmental variables processed via Google Earth Engine.

Methods

The MaxEnt model was applied using historical fire occurrence data from MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite), combined with environmental predictors like climate, topography and vegetation. Key predictors were statistically analyzed for relevance, ensuring the model’s robustness. The output was validated using independent fire data.

Key results

The model achieved an area under the curve score of 0.79, an accuracy of 72% and recall of 71%. The resulting map delineates fire risk zones, identifying high-risk areas, predominantly in forested and steppe regions.

Conclusions

The study highlights the efficacy of the MaxEnt model in wildfire risk prediction, underscoring its potential for application in other regions.

Implications

The map provides critical insights for resource allocation, fire management strategies and policymaking. Continuous model refinement and integration of real-time data are recommended to enhance predictive accuracy and adaptability.

Keywords: Geographic Information System (GIS), Google Earth Engine (GEE), Kazakhstan, machine learning, maximum entropy, MaxEnt, remote sensing, wildfire risk.

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