Discovering the relationship of disasters from big scholar and social media news datasets

The construction method for chains of disasters or events is still one of the core scientific questions in studying the common rules of disaster’s evolution.Especially when dealing with the complexity and diversity of disasters,it is critical to make a further investigation on reducing the dependency of prior knowledge and supporting the comprehensive chains of disasters.This paper tries to propose a novel approach,through collecting the big scholar and social news data with disasterrelated keywords,analysing the strength of their relationships with the co-word analysis method,and constructing a complex network of all defined disaster types,in order to finally intelligently extract the unique disaster chain of a specific disaster type.Google Scholar,Baidu Scholar and Sina News search engines are employed to acquire the needed data,and the respectively obtained disaster chains are compared with each other to show the robustness of our proposed approach.The achieved disaster chains are also compared with the ones concluded from existing research methods,and the very reasonable result is demonstrated.There is a great potential to apply this novel method in disaster management domain to find more secrets about disasters.

Landform classification based on optimal texture feature extraction from DEM data in Shandong Hilly Area, China

Texture and its analysis methods are crucial for image feature extraction and classification. Digital elevation model (DEM) is the most important data source of digital terrain analysis and landform classification, and considerable research values are gained from texture feature extraction and analysis from DEM data. In this research, on the basis of optimal texture feature extraction, the hilly area in Shandong, China, was selected as the study area, and DEM data with a resolution of 500 m were used as the experimental data for landform classification. First, second-order texture measures and texture image were extracted from DEM data by using a gray level cooccurrence matrix (GLCM). Second, the variation characteristics of each texture measure were analyzed, and the optimal feature parameters, such as direction, gray level, and texture window, were determined. Meanwhile, the texture feature value, combined with maximum information, was calculated, and the multiband texture image was obtained by resolving three optimal texture measure images. Finally, a support vector machine (SVM) method was adopted to classify landforms on the basis of the multiband texture image. Results indicated that the texture features of DEM data can be sufficiently represented and measured via the quantitative GLCM method. However, the feature parameters during the texture feature value calculation required further optimization. Based on the image texture from DEM data, efficient classification accuracy and ideal classification effect were achieved.