Communication science et technologie
Volume 23, Numéro 1, Pages 13-22
2025-07-23
Predicting Fire Forest In Algeria : A New Approach
Authors : El Bouhissi Houda . Illoul Naima .
Abstract
Forest fires have emerged as a major concern, drawing international attention—especially in Algeria. They are increasingly recognized by the global community as one of the most critical security challenges of our time. This study examines the current protective measures in place to combat these fires and evaluates their effectiveness in preserving the country’s environment from devastating damage. Numerous forecasting methods exist, with those leveraging artificial intelligence—particularly machine learning and related technologies—being the most widely used. These AI- driven techniques have led to the development of adaptive and reliable systems across various domains, especially in predictive modeling. In this work, we apply such methods to forecast forest fires. The aim of this paper is to introduce a novel hybrid approach that combines machine learning with bioinspired algorithms to enhance forest fire prediction. Experimental results demonstrate that integrating bioinspired algorithms significantly improves the performance of machine learning models.
Keywords
machine learning ; Kaggle ; forest fire ; prediction ; logistic regression
Les articles similaires
Ridha Ilyas Bendjillali
.
Mohammed Sofiane Bendelhoum
.
Ali Abderrazak Tadjeddine
.
Miloud Kamline
.
pages 144-152.
Boukrouma Houcem Eddine
.
pages 8-13.
بوسكرة بوعلام
.
ص 438-452.
Messaoudi Malika
.
pages 391-410.
Mezouaghi Djilali
.
Belfodil Kamel
.
pages 363-383.
