Incorporating Dynamic Factors in Geological Hazard Risk Assessment: Integrating InSAR Deformation and Rainfall Conditions

Atmosphere, Journal Year: 2025, Volume and Issue: 16(4), P. 360 - 360

Published: March 22, 2025

Geological hazards, particularly in mountainous regions, represent significant threats to life, property, and the environment. In this study, we focus on Luoping County, Yunnan Province, China, employing SBAS-InSAR technology monitor surface deformation between 8 October 2022 27 September 2024. By integrating InSAR data with 10 static disaster-causing factors, including elevation, slope, aspect, curvature, distance faults, rivers, roads, engineering geological rock groups, geomorphological types, NDVI, hazard susceptibility was assessed using information value (IV) model value–random forest (IV-RF) coupled model. Accuracy validation ROC curves indicated that IV-RF model, integrated data, achieved highest accuracy, an AUC of 0.805. Based evaluation, rainfall intensity introduced as a triggering factor assess risks under four conditions: 10-year, 20-year, 50-year, 100-year return periods. The results demonstrated incorporating significantly improved disaster prediction providing more reliable sustainable risk assessment outcomes. This study underscores critical role technology, combined conditions, enhancing precision assessments, offering scientific basis for prevention mitigation strategies County similar regions.

Language: Английский

Advancements in Artificial Intelligence Applications for Forest Fire Prediction

Forests, Journal Year: 2025, Volume and Issue: 16(4), P. 704 - 704

Published: April 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

Language: Английский