R.V. da Silva

Bio: R.V. da Silva is an academic researcher from Universidade Federal da Fronteira Sul. The author has contributed to research in topics: Landslide & Prognostics. The author has an hindex of 5, co-authored 12 publications receiving 95 citations.

Artificial neural network ensembles applied to the mapping of landslide susceptibility

Abstract: This study proposes a comprehensive methodology to the application of an Artificial Neural Network Ensemble (ANNE) for the mapping of landslide susceptibility. The identification of susceptible areas was performed on the basis of landslide inventory databases and seven parameters from three classes: geomorphological (elevation, aspect, slope, topographic moisture index, profile curvature),geological (lithology) and environmental (land use). Studies are presented for two major cities in Brazil, Porto Alegre and Rio de Janeiro. As the main result, we show that the susceptibility maps generated by the ANNE feature higher accuracy than those published by official organs such as the Brazilian Geological Survey and Geotechnical Institute Foundation (Geo-Rio). This indicates that the proposed methodology can be an effective tool to assist the development of reliable landslide susceptibility maps in an efficient and agile manner.

Landslide susceptibility mapping with r.landslide: A free open-source GIS-integrated tool based on Artificial Neural Networks

Abstract: This study presents r.landslide, a free and open source add-on to the open source Geographic Information System (GIS) GRASS software for landslide susceptibility mapping. The tool was written in Python language and works on the top of an Artificial Neural Network (ANN) fed with environmental parameters and landslide databases. In order to illustrate the application and effectiveness of the developed tool, a case study is presented for the municipality of Porto Alegre, Brazil. The resulting landslide susceptibility maps are compared with the map published by the Brazilian Geological Survey (CPRM) and a direct comparison using unseen (new) landslide records indicate that the r.landslide can identify and pinpoint susceptible areas with better accuracy. The module can be used by natural disaster management bodies and land use planning organs as a support tool for the elaboration of landslide susceptibility maps in an agile and efficient manner.

Convolutional neural networks applied to semantic segmentation of landslide scars

Abstract: Landslides are considered to be among the most alarming natural hazards. Therefore, there is a growing demand for databases and inventories of these events worldwide, since they are a vital resource for landslide risk assessment applications. Given the recent advances in the field of image processing, the objective of this study is to evaluate the performance of a deep convolutional neural network architecture called U-Net for the mapping of landslide scars from satellite imagery. The question that drives the study is: can fully convolutional neural networks be successfully applied as the backbone of automatic frameworks for building landslide inventories, keeping or improving the identification accuracy and agility when compared to other methods? To seek for an answer to it, scenes from the Landsat-8 satellite of a region of Nepal were obtained and processed in order to compose a landslide image database that served as the basis for the training, validation and test of deep convolutional neural networks. The U-Net architecture was applied and the results indicate that it has the potential to identify landslide scars, improving over previously published research on the topic for the same study region. The validation process resulted in recall, precision and F1-score values of 0.74, 0.61 and 0.67, respectively, thus higher than those from previous studies using different methodologies. The results indicate the potential of the method to be applied in dynamic mapping systems for landslide scar identification, which paves the way to the composition and updating of landslide scar databases. These, in turn, can support a great deal of quantitative landslide susceptibility mapping methods that heavily rely on data to provide accurate results.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area

Abstract: This research aims at investigating the capability of Keras’s deep learning models with three robust optimization algorithms (stochastic gradient descent, root mean square propagation, and adaptive moment optimization) and two-loss functions for spatial modeling of landslide hazard at a regional scale. Shallow landslides at the Ha Long area (Vietnam) were selected as a case study. For this regard, set of ten influencing factors (slope, aspect, curvature, topographic wetness index, landuse, distance to road, distance to river, soil type, distance to fault, and lithology) and 193 landslide polygons were prepared to construct a Geographic Information System (GIS) database for the study area. Using the collected database, the DNN with its potential of realizing complex functional mapping hidden in the data is used to generalize a decision boundary that separates the learning space into two distinct categories: landslide (a positive class) and non-landslide (a negative class). Experimental results point out that the utilized the Keras’s deep learning model with the Adam optimization and the mean squared error lost function is the best with the prediction performance of 84.0%. The performance is better than those of the employed benchmark approaches of random forest, J48 decision tree, classification tree, and logistic model tree. We conclude that the Keras’s deep learning model is a new tool for shallow susceptibility mapping at landslide-prone areas.