GIS-based landslide susceptibility modeling using data mining techniques

Introduction: Landslide is one of the most widespread geohazards around the world. Therefore, it is necessary and meaningful to map regional landslide susceptibility for landslide mitigation. In this research, landslide susceptibility maps were produced by four models, namely, certainty factors (CF), naive Bayes (NB), J48 decision tree (J48), and multilayer perceptron (MLP) models.Methods: In the first step, 328 landslides were identified via historical data, interpretation of remote sensing images, and field investigation, and they were divided into two subsets that were assigned different uses: 70% subset for training and 30% subset for validating. Then, twelve conditioning factors were employed, namely, altitude, slope angle, slope aspect, plan curvature, profile curvature, TWI, NDVI, distance to rivers, distance to roads, land use, soil, and lithology. Later, the importance of each conditioning factor was analyzed by average merit (AM) values, and the relationship between landslide occurrence and various factors was evaluated using the certainty factor (CF) approach. In the next step, the landslide susceptibility maps were produced based on four models, and the effect of the four models were quantitatively compared by receiver operating characteristic (ROC) curves, area under curve (AUC) values, and non-parametric tests.Results: The results demonstrated that all the four models can reasonably assess landslide susceptibility. Of these four models, the CF model has the best predictive performance for the training (AUC=0.901) and validating data (AUC=0.892).Discussion: The proposed approach is an innovative method that may also help other scientists to develop landslide susceptibility maps in other areas and that could be used for geo-environmental problems besides natural hazard assessments

Two Rab5 Homologs Are Essential for the Development and Pathogenicity of the Rice Blast Fungus Magnaporthe oryzae

The rice blast fungus, Magnaporthe oryzae, infects many economically important cereal crops, particularly rice. It has emerged as an important model organism for studying the growth, development, and pathogenesis of filamentous fungi. RabGTPases are important molecular switches in regulation of intracellular membrane trafficking in all eukaryotes. MoRab5A and MoRab5B are Rab5 homologs in M. oryzae, but their functions in the fungal development and pathogenicity are unknown. In this study, we have employed a genetic approach and demonstrated that both MoRab5A and MoRab5B are crucial for vegetative growth and development, conidiogenesis, melanin synthesis, vacuole fusion, endocytosis, sexual reproduction, and plant pathogenesis in M. oryzae. Moreover, both MoRab5A and MoRab5B show similar localization in hyphae and conidia. To further investigate possible functional redundancy between MoRab5A and MoRab5B, we overexpressed MoRAB5A and MoRAB5B, respectively, in MoRab5B:RNAi and MoRab5A:RNAi strains, but neither could rescue each other’s defects caused by the RNAi. Taken together, we conclude that both MoRab5A and MoRab5B are necessary for the development and pathogenesis of the rice blast fungus, while they may function independently

Joint superpixel and Transformer for high resolution remote sensing image classification

Abstract Deep neural networks combined with superpixel segmentation have proven to be superior to high-resolution remote sensing image (HRI) classification. Currently, most HRI classification methods that combine deep learning and superpixel segmentation use stacking on multiple scales to extract contextual information from segmented objects. However, this approach does not take into account the contextual dependencies between each segmented object. To solve this problem, a joint superpixel and Transformer (JST) framework is proposed for HRI classification. In JST, HRI is first segmented into superpixel objects as input, and Transformer is used to model the long-range dependencies. The contextual relationship between each input superpixel object is obtained and the class of analyzed objects is output by designing an encoding and decoding Transformer. Additionally, we explore the effect of semantic range on classification accuracy. JST is also tested by using two HRI datasets with overall classification accuracy, average accuracy and Kappa coefficients of 0.79, 0.70, 0.78 and 0.91, 0.85, 0.89, respectively. The effectiveness of the proposed method is compared qualitatively and quantitatively, and the results achieve competitive and consistently better than the benchmark comparison method