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April 8, 2026Open Access

Artificial Neural Network-Based Forest Fire Spread Prediction Models in the Mediterranean Climate Zone: Simulation Accuracy of Wind-Topography Interaction

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Authors

KAKaan Alper

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Overview

Demonstrates how ANN models predict fire spread in Mediterranean forests, suggesting improvements for early warning systems.

Key Points

  • The aim is to evaluate ANN-based models for predicting forest fire spread under the influence of wind and topography in Mediterranean regions.
  • Evaluated multilayer perceptron, convolutional neural networks, and recurrent neural networks.
  • Compared model performance using error metrics like RMSE and R².
  • Analyzed model accuracy across varying slopes and wind conditions.
  • The CNN-LSTM hybrid model showed superior performance with R² = 0.91 and RMSE = 12.4 m.
  • Accuracy declines on slopes greater than 35% and during abrupt wind direction changes.

Cite This Study

Kaan Alper (2026) studied this question.

synapsesocial.com/papers/69d5f10974eaea4b11a7a889https://doi.org/10.5281/zenodo.19441675
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Computational Performance of Physics-Informed Neural Networks Versus the Rothermel Model in Wind-Topography-Fuel Interaction-Based Wildfire Spread Prediction2026
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  3. 3Wind Speed Prediction Based on AM-BiLSTM Improved by PSO-VMD for Forest Fire Spread2026
  4. 4Integrating Human Domain Knowledge into Artificial Intelligence for Hybrid Forest Fire Prediction: Case Studies from South Korea and Italy2024
  5. 5Data-Driven Wildfire Spread Modeling of European Wildfires Using a Spatiotemporal Graph Neural Network2024 · 6 citations