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On March 15, 2024, a wildfire occurred in Yajiang County, Sichuan Province, China, burned an area of 278.8 km2 and influenced 250 catchments in the burned area. On April 23 and May 10–11, 2024, two rainfall events struck the burned area, inducing 8 and 54 post-fire debris flows, respectively. This news investigates the characteristics of the burned area, involving the burn severity mapping, and geometry analysis on the burned catchments. Then, the characteristics of the debris flows following the fire are unravelled based on preliminary field investigations. It is expected that the debris flows after the wildfire, particularly in the first rainy season, with the high susceptibility and low rainfall threshold, will attract researchers' interest in monitoring and mitigating such unique debris flow disasters. [ABSTRACT FROM AUTHOR]

Machine learning (ML)-based prediction models for mapping hazard (e.g., landslide and debris flow) susceptibility have been widely developed in recent research. However, in some specific areas, ML models have limited application because of the uncertainties in identifying negative samples. The Parlung Tsangpo Basin exemplifies a region prone to recurrent glacial debris flows (GDFs) and is characterized by a prominent landform featuring deep gullies. Considering the limitations of the ML model, we developed and compared two combined statistical models (FA-WE and FA-IC) based on factor analysis (FA), weight of evidence (WE), and the information content (IC) method. The final GDF susceptibility maps were generated by selecting 8 most important static factors and considering the influence of precipitation. The results show that the FA-IC model has the best performance. The areas with a very high susceptibility to GDFs are primarily located in the narrow valley section upstream, on both sides of the valley in the middle and downstream of the Parlung Tsangpo River, and in the narrow valley section of each tributary. These areas encompass 86 gullies and are characterized as "narrow and steep". [ABSTRACT FROM AUTHOR]

Dempster–Shafer evidence theory is an effective method to deal with information fusion. However, how to deal with the fusion paradoxes while using the Dempster's combination rule is still an open issue. To address this issue, a new basic probability assignment (BPA) generation method based on the cosine similarity and the belief entropy was proposed in this paper. Firstly, Mahalanobis distance was used to measure the similarity between the test sample and BPA of each focal element in the frame of discernment. Then, cosine similarity and belief entropy were used respectively to measure the reliability and uncertainty of each BPA to make adjustments and generate a standard BPA. Finally, Dempster's combination rule was used for the fusion of new BPAs. Numerical examples were used to prove the effectiveness of the proposed method in solving the classical fusion paradoxes. Besides, the accuracy rates of the classification experiments on datasets were also calculated to verify the rationality and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]