Advancements in Artificial Intelligence Applications for Forest Fire Prediction

Forests, Год журнала: 2025, Номер 16(4), С. 704 - 704

Опубликована: Апрель 19, 2025

In recent years, the increasingly significant impacts of climate change and human activities on environment have led to more frequent occurrences extreme events such as forest fires. The recurrent wildfires pose severe threats ecological environments life safety. Consequently, fire prediction has become a current research hotspot, where accurate forecasting technologies are crucial for reducing economic losses, improving management efficiency, ensuring personnel safety property security. To enhance comprehensive understanding wildfire research, this paper systematically reviews studies since 2015, focusing two key aspects: datasets with related tools algorithms. We categorized literature into three categories: statistical analysis physical models, traditional machine learning methods, deep approaches. Additionally, review summarizes data types open-source used in selected literature. further outlines challenges future directions, including exploring risk multimodal learning, investigating self-supervised model interpretability developing explainable integrating physics-informed models constructing digital twin technology real-time simulation scenario analysis. This study aims provide valuable support natural resource enhanced environmental protection through application remote sensing artificial intelligence

Язык: Английский

Beyond Description: Unlocking the Predictive Potential of African Ecology

African Journal of Ecology, Год журнала: 2025, Номер 63(3)

Опубликована: Апрель 1, 2025

ABSTRACT Ecology's strength lies in its ability to explain and predict interactions between organisms their environment. However, African ecological research has historically been dominated by descriptive studies, focusing on biodiversity patterns, species distributions, behavioural observations or monitoring of large mammal populations (especially East savannahs). This pattern also traditionally characterised the studies community ecology. While valuable, these often fall short providing predictive insights essential for addressing pressing challenges such as climate change, ecosystem resilience. We advocate a paradigm shift ecology—moving beyond description hypothesis‐driven, research. Community ecology Africa can transcend documentation uncover mechanisms underlying processes integrating methodologies null models, Monte Carlo simulations modelling based upon data mining techniques. Predictive interactions, assembly functions have potential enhance both theoretical applied science, ensuring global relevance. Curriculum reforms statistics methodological training academic institutions will be crucial fostering this transformation. As Journal Ecology seeks champion transition, we urge researchers embrace frameworks that not only document but provide actionable into dynamics. could achieved re‐analysing long‐term sets published several less‐distributed journals, other languages than English. is critical positioning at forefront international discourse, driving impactful conservation management strategies.

Язык: Английский