Comparative analysis of classical and deep machine learning architectures in wildfire forecasting

Andrey Goncharov; Andrey Balobanov; Alexandr Matveev

UDC

004.8 614.8

COMPARATIVE ANALYSIS OF CLASSICAL AND DEEP MACHINE LEARNING ARCHITECTURES IN WILDFIRE FORECASTING Comparative analysis of classical and deep machine learning architectures in wildfire forecasting

АА Аа

Published in National Security and Strategic Planning · Issue 1, 2026 · Pages 78–89 · Rubric: Technosphere safety, ecology of living systems and rational nature management

Received: 16.02.2026 Accepted: 21.03.2026 Published: 30.04.2026

Authors

Andrey Goncharov

Andrey Balobanov

ORCID SPIN

Alexandr Matveev

ORCID SPIN

Andrey Goncharov

Saint-Petersburg university of State fire service of EMERCOM of Russia department of systems analysis and crisis management · Russian Federation

Andrey Balobanov

Academic degree: candidate of technical sciences

Saint Petersburg university of State fire service of EMERCOM of Russia

ORCID SPIN

Alexandr Matveev

Academic degree: candidate of technical sciences

Science rang: docent

Saint-Petersburg university of State fire service of EMERCOM of Russia head of the department · department of applied mathematics and information technology security · Russian Federation · Saint-Petersburg

ORCID SPIN

ABSTRACT

This article examines the problem of probabilistic wildfire forecasting with a 24-hour and 7-day lead time based on ground-based meteorological observations and satellite monitoring. A feature space was constructed consisting of lagged meteorological, exponentially weighted average, calendar, and satellite features. To solve the problem, a gradient boosting model (CatBoost), as a representative of classical machine learning, and a recurrent neural network (LSTM), as a representative of deep learning architectures, were compared. Model performance was assessed using standard metrics such as ROC-AUC, PR-AUC, F1, Precision, and Recall. Statistical robustness was assessed using bootstrap analysis with 95% confidence intervals. For additional evaluation, probabilities were calibrated using isotonic regression and Platt scaling, and an analysis of seasonal quality indicators was conducted. A study of the interpretability of the models was conducted using SHAP analysis for CatBoost and the Integrated Gradient attribution method for LSTM. It was found that CatBoost provides a more balanced forecast quality and demonstrates higher sensitivity, but is inferior in the accuracy and reliability of probabilistic estimates.

KEYWORDS

wildfires, machine learning, CatBoost, LSTM, fire forecasting

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Chairman of editorial board

Vladimir Matveev

070355mvv@gmail.com +7(911)791-08-80

GOST

Goncharov A. S., Balobanov A. A., Matveev A. V. Comparative analysis of classical and deep machine learning architectures in wildfire forecasting // National Security and Strategic Planning. 2026. no. 1. pp. 78-89. (Date of access 20.08.2026).

MLA

Andrey Goncharov, Balobanov A. A., Matveev A. V.. "Comparative analysis of classical and deep machine learning architectures in wildfire forecasting." National Security and Strategic Planning 2026.1 (2026): 78-89.

APA

Goncharov A. S., Balobanov A. A., Matveev A. V. (2026). Comparative analysis of classical and deep machine learning architectures in wildfire forecasting. National Security and Strategic Planning, 2026(1), 78-89.

Chicago

WIKI

{{cite journal | author = Goncharov A. S., Balobanov A. A., Matveev A. V. | title = Comparative analysis of classical and deep machine learning architectures in wildfire forecasting | journal = National Security and Strategic Planning | year = 2026 | volume = 2026 | issue = 1 | pages = 78-89 }}

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COMPARATIVE ANALYSIS OF CLASSICAL AND DEEP MACHINE LEARNING ARCHITECTURES IN WILDFIRE FORECASTING Comparative analysis of classical and deep machine learning architectures in wildfire forecasting

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