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A Model for Boat Accident Prediction Using Geographical and Topological Data

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Abstract

In recent years, natural calamities and disasters have inflated day by day and people have suffered the results of them. One of the foremost reasons being, lack of correct data which can be reached to individuals. Google Maps is growing speedily within the world, attributable to the supply of geospatial data. It is also because of the increase in devices which will benefit from such data, specifically, Global Positioning System (GPS) enabled devices. The catholicity of hand-held computing devices has been found to be particularly economical and effective in conducting disaster management and relief operations. Thus the associate application is in want which may alert users once there is a stroke of natural bad luck in their section. By knowing beforehand, the users of that application will take acceptable measures to avoid wasting themselves and scale back the dependence on search and rescue groups. Throughout this process there is a stroke of bad luck and results of their experiences may be used elsewhere for providing alerts. Such associate application eliminates the necessity for rescue since the users are supplied with alerts if they are in a very bad luck zone. Thus users may be tuned in to the danger they're in and take necessary measures. APSDMA (Andhra Pradesh State Disaster Management Authority) is making an attempt to help the individuals by providing the information of the natural disaster at a selected space and alert the individuals at the particular space. In-order to provide such alert information, APSDMA needs a platform. This paper presents the Webpage that is developed with the requirements given by APSDMA for the day to day actions.

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Correspondence to S. Vasavi.

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This article is part of the topical collection “Data Science and Communication” guest edited by Kamesh Namudri, Naveen Chilamkurti, Sushma S J and S. Padmashree.

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Vasavi, S., LeelaBhavani, A., Badeti, P. et al. A Model for Boat Accident Prediction Using Geographical and Topological Data. SN COMPUT. SCI. 2, 131 (2021). https://doi.org/10.1007/s42979-021-00517-8

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