With continuous growth of web applications around the globe, it is a challenge to find the suitable information needed for the user in a limited time.Number of handheld mobile devices is increasing and most of the business revolves around the correct search of the data. Without a proper recommender system it is very difficult to get required information from the web applications. Web applications use recommender systems to provide suitable data to users based on their choices and interests. For different kinds of needs different types of recommender systems have been proposed. Two most basic types of recommender systems are collaborative filtering recommender system and content based recommender system. Sometimes these two recommender systems are combined to increase the efficiency of a recommender system The generated new recommender system is known as hybrid recommender system.
The purpose of this paper is to help readers understand the basics of recommender systems. This paper identifies key areas of research openly available for new researchers. After reading this paper new researchers can understand basic problems of recommender systems which need improvement and hence they can make those problems their area of research.
Microwave satellite data enable the mapping of flooding over large areas, irrespective of cloud and weather conditions. Efficient planning of microwave data for acquiring satellite images over flood-affected areas for flood assessment and mapping needs prior information about the probable flood areas. Analysis of rainfall trend over a particular region over a time can help in predicting the flood-like situation and probable flood areas. Forecasts from weather forecasts models provide a fair idea about high rainfall events. Integration of global rainfall products (Tropical Rainfall Measuring Mission (TRMM)) and global forecast precipitation products (Global Ensemble Forecast System (GEFS)) can help in identifying the probable areas which may attain high rainfall. This advance information about probable high rainfall areas can be used in planning the remote sensing satellites for acquiring the satellite imagery over the area for flood mapping activities. Temporal satellite images integrated with disaster news available on Internet can help in analysing the ground situation (residing or increasing of flood situation). It can help in resource planning and management. The main purpose of this study is to develop a GIS-based framework coupled with automatic procedures for integrating TRMM, GEFS and disaster news using opensource technologies to identify the probable high rainfall area over Indian region and its application in planning acquisition of microwave satellite data sets for flood mapping activities. The high rainfall events identified using TRMM, GEFS and disaster news have been verified with Indian Metrological Department rainfall data during Chennai floods 2015 and Assam floods 2016. The system has been successfully used to plan and acquired satellite data for flood season 2015 and 2016.
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