Suchergebnisse

Abstract. Rainfall is intrinsically linked to the occurrence of landslide catastrophes. Identifying the most suitable rainfall threshold model for an area is crucial for establishing effective daily landslide hazard warnings, which are essential for the precise prevention and management of local landslides. This study introduces a novel approach that utilizes multilayer perceptron (MLP) regression to calculate rainfall thresholds for 453 rainfall-induced landslides. This research represents the first attempt to integrate MLP and ordinary least squares methods for determining the optimal rainfall threshold model tailored to distinct subregions, categorized by topographical and climatic conditions. Additionally, an innovative application of a three-dimensional convolutional neural network (CNN-3D) model is introduced to enhance the accuracy of landslide susceptibility predictions. Finally, a comprehensive methodology is developed to integrate daily rainfall warning levels with landslide susceptibility predictions using a superposition matrix, thus offering daily landslide hazard warning results for the study area. The key findings of this study are as follows. (1) The optimal rainfall threshold models and calculation methods vary across different subregions, underscoring the necessity for tailored approaches. (2) The CNN-3D model substantially improves the accuracy of landslide susceptibility predictions. (3) The daily landslide hazard warnings were validated using anticipated rainfall data from 19 July 2020, thereby demonstrating the reliability of both the landslide hazard warning results and the rainfall threshold model. This study presents a substantial advancement in the precise prediction and management of landslide hazards by employing innovative modeling techniques.

Wei Xiong,1 Mei Xu,2 Yunfeng Zhao,3 Xueling Wu,4 Bigyan Pudasaini,1 Jin-ming Liu1 1Department of Cardio-Pulmonary Circulation, Shanghai Pulmonary Hospital, Tongji University School of Medicine, 2Department of Pediatrics, Gongli Hospital, Second Military Medical University, 3Department of Respiratory Medicine, Punan Hospital, 4Department of Respiratory Medicine, Renji Hospital, Jiaotong University School of Medicine, Shanghai, People's Republic of China Background: The major characteristic of COPD is systemic inflammation. The parameters such as neutrophil-to-lymphocyte ratio (NLR) and eosinophil-to-basophil ratio (EBR) in routine blood test (RBT) are considered to be the underlying biomarkers of inflammation. We hypothesized that the prognosis of patients with COPD can be predicted with RBT. Methods: Patients with COPD in stable stage were enrolled. The RBT, pulmonary function testing (PFT), BODE index, C-reactive protein (CRP), procalcitonin, and erythrocyte sedimentation rate (ESR) were performed at enrollment and every follow-up once in every 3 months during the 24-month follow-up period. Meanwhile, exacerbation count and mortality incidence were recorded. The correlation between the prognostic biomarkers and the prognosis of patients was analyzed. Results: The NLR and EBR in RBT have a significant correlation with the severity of patients with COPD. The NLR is an independent predictor for mortality and the EBR is an independent predictor for exacerbation. Conclusion: As an inexpensive, accessible, and convenient assay, RBT may be used as a practical means in the prediction of prognosis of patients with COPD in future clinical settings. Keywords: COPD, prognosis, inflammation, routine blood test, neutrophil-to-lymphocyte ratio, eosinophil-to-basophil ratio