The application of machine learning (ML) and remote sensing (RS) in soil and water
conservation has become a powerful tool. As analytical tools continue to advance, the …

Real-time detection of forest fires through remote sensing is a challenging task, especially in
the context of limited data availability. In response to this challenge, this article leverages the …

This study developed three hybrid light gradient boosting machine (LightGBM) models using
novel metaheuristic algorithms (golden jackal optimization [GJO], the pelican optimization …

Wildfires pose an increasing risk to expanding urban population centers, and to critical
habitats for plant and animal species. Improving current wildland management strategies …

In the context of climate change, wildfires occur more frequently and significantly impact the
vegetation–soil–water continuum. Soil water is a critical factor for understanding wildfire …

Recurrent wildfires pose an immense and urgent global challenge, as they endanger human
lives and have significant consequences on society and the economy. In recent years …

For effective management and prevention, wildfire risk prediction needs to consider the
substantial impacts of climate change on wildfire patterns. This study analyzed the …

In the context of the current nature crisis, being able to reliably and cost‐effectively track
subtle changes in the biosphere across adequate spatial and temporal extents and …

This study presents an improved methodology of wildfire detection in the forest using
PyTorch, a wise machine learning framework, and the Flask application for real-time …

This thesis investigates the integration of artificial intelligence (AI), particularly large
language models, into crisis communication, focusing on how AI technologies can enhance …