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Landslides are major natural hazards which not only result in the loss of human life but also cause economic burden on the society. Therefore, it is essential to develop suitable models to evaluate the susceptibility of slope failures and their zonations. This paper scientifically assesses various methods of landslide susceptibility zonation in GIS environment. A comparative study of Weights of Evidence (WOE), Analytical Hierarchy Process (AHP), Artificial Neural Network (ANN), and Generalized Linear Regression (GLR) procedures for landslide susceptibility zonation is presented. Controlling factors such as lithology, landuse, slope angle, slope aspect, curvature, distance to fault, and distance to drainage were considered as explanatory variables. Data of 151 sample points of observed landslides in Mazandaran Province, Iran, were used to train and test the approaches. Small scale maps (1:1,000,000) were used in this study. The estimated accuracy ranges from 80 to 88 percent. It is then inferred that the application of WOE in rating maps’ categories and ANN to weight effective factors result in the maximum accuracy.

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Air pollution is a serious challenge in densely populated cities. It poses a significant threat to human health, property and the environment throughout the developed and developing parts of the world. Real-time air quality monitoring and public access to related information are the key components of a successful environmental management. Mashups can be customized to adequately address the monitoring of such geographically oriented challenges. The growth of mashups has been accelerated by Web 2.0 technologies. The integration of Web 2.0 and GIS (Geographic Information System) has been highlighted by the second generation of Internet-based services that emphasizes on online information collaboration and sharing among users. The main objective of this paper is to assess, design and develop a Web 2.0 thin client application called Tehran Air Quality Reporter. The application uses Google Maps API (Application Programming Interface), Web GIServices (Geographic Information Services), and AJAX (Asynchronous JavaScript and XML) to disseminate real-time air quality information through internet. Such information can improve the decisions of the pertinent environmental organizations as well as urban settlers. The software also utilized DOM (Document Object Model) and JavaScript functionalities for handling the response events and providing flexibility and more interactivity. The developed Geo Mashup includes geospatial maps and features, standard business charts, node and link displays, as well as custom visual displays. All visualization components run in any web browsers and provide a user friendly environment.