Using visualization and predictive analysis to predict train delays

France has the second largest European railway network, with a total of 29,901 kilometers of railway. However, the travel experience of passengers is frequently marked by delays, late arrival of trains at stations, causing inconvenience. The purpose of this paper is to present a new approach for visual prediction of train delays. Our approach is driven by predictive analysis and interactive visualization. The study has benefitted from access to open data SNCF including information about train delays , train number , station , departure and arrival time .Based on this data we develop a new workflow for predictive analysis including visualization in all steps from data pre-processing to deployment

A new architecture for monitoring land use and land cover change based on remote sensing and GIS: A data mining approach

The issue of land use (LU) and land cover change (LCC) has become crucial around the world in recent years, not only for researchers, but also for urban planners and environmentalists who advocate sustainable land use in the future. In Morocco, this phenomenon affects large areas and is all the more pronounced because the climate is arid with cycles of increasing drought and soils are poor and highly vulnerable to erosion. In addition, the precarious living conditions of rural populations pushes them to over exploit natural resources to meet their growing needs, which further amplifies environmental degradation. In this LU/LCC monitoring context, this paper aims on one hand at giving a clear survey of classical methods and techniques used to monitor LU/LCC, on other hand the authors propose a new architecture whose objective is to integer data mining techniques to the LU/LCC monitoring in order to automatically and efficiently improve the monitoring, control and asset management in LU/LC