Geographically Weighted Random Forest Based on Spatial Factor Optimization for the Assessment of Landslide Susceptibility DOI Creative Commons

Feifan Lu,

Guifang Zhang, Tonghao Wang

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(9), С. 1608 - 1608

Опубликована: Май 1, 2025

Landslide susceptibility mapping is a crucial tool for landslide disaster risk management. However, the spatial heterogeneity of conditioning factors affects accuracy predictions. This study proposes novel method combining GeoDetector and geographical weighted random forest (GeoD-GWRF), local machine learning approach. The GeoD-GWRF model can select from perspective differentiation interpret influence on landslides at scale. model’s applicability verified using Luhe County, Guangdong Province, as case study. Compared to traditional model, achieves higher prediction (AUC = 0.942). In addition, applicable broader areas provide more targeted results. offers valuable reference exploring in mapping.

Язык: Английский

Study on the temporal pattern and county-scale comprehensive risk assessment of wildfires in Sichuan Province DOI
Weiting Yue, Yunji Gao, Yao Xiao

и другие.

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Апрель 24, 2025

Abstract Climate change and increased human activity have resulted in an increase the frequency intensity of wildfires. Effective wildfire risk assessment is essential for disaster prevention, resource protection, regional stability. Existing studies often overlook spatial heterogeneity temporal patterns wildfires, with limited county-scale quantitative assessments. To address these gaps, multidimensional framework Sichuan Province was proposed, combining characterization modeling. Temporal trends mutation wildfires from 2001 to 2023 were analyzed using Mann-Kendall test. Additionally, model constructed by hazard vulnerability Specifically, assessed Multiscale Geographically Weighted Regression (MGWR) capturing driving factors. Vulnerability through Multi-Criteria Decision Analysis (MCDA) approach identify areas high their factor importance. The results indicated a significant rise particularly during winter non-fire prevention periods. MGWR effectively captured heterogeneity, identifying highest levels southwestern Sichuan, Liangshan Prefecture Panzhihua City. High scattered, mainly across southwestern, southern, northern Sichuan. integrated revealed that its surrounding counties exhibited significantly higher than other regions, while eastern northeastern regions demonstrated lowest risk. This study provides scientific foundation targeted management, emergency response strategies Province, offering valuable insights policymakers managers.

Язык: Английский

Процитировано

0

Geographically Weighted Random Forest Based on Spatial Factor Optimization for the Assessment of Landslide Susceptibility DOI Creative Commons

Feifan Lu,

Guifang Zhang, Tonghao Wang

и другие.

Remote Sensing, Год журнала: 2025, Номер 17(9), С. 1608 - 1608

Опубликована: Май 1, 2025

Landslide susceptibility mapping is a crucial tool for landslide disaster risk management. However, the spatial heterogeneity of conditioning factors affects accuracy predictions. This study proposes novel method combining GeoDetector and geographical weighted random forest (GeoD-GWRF), local machine learning approach. The GeoD-GWRF model can select from perspective differentiation interpret influence on landslides at scale. model’s applicability verified using Luhe County, Guangdong Province, as case study. Compared to traditional model, achieves higher prediction (AUC = 0.942). In addition, applicable broader areas provide more targeted results. offers valuable reference exploring in mapping.

Язык: Английский

Процитировано

0