Landslide Prediction Using Classifier Models

Paul, Sukriti and Garg, Arpita and Chethan, S (2018) Landslide Prediction Using Classifier Models. Journal of Engineering and Applied Sciences, 13 (1). pp. 2301-2308. ISSN 1819-6608

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Prediction systems have been flooding the IT industry ever since the concept of information retrieval came into existence. Every organization uses one of these prediction systems which helps the users of the system to predict useful information on the basis of their past records. The landslide prediction system aims to use this concept for not just different places but different terrains as well. NASA maintains the record of the landslides that have happened in the past, this record includes details such as area, terrain, rainfall, population, vegetation cover, etc. If this data can be classified on the basis of the patterns mined, it becomes very easy for the authorities to inform the people living in a particular area about landslides or development of a colony on a landslide prone area can be avoided. Not only that, reasons for landslide occurrence can be taken note off and be avoided. For example, lack of vegetation is causing landslides in an area. If this data is provided and wisely used, lives lost can be greatly reduced. This study aims to find ways to obtain this data.

Item Type: Article
Uncontrolled Keywords: Landslide, prediction, Naive Bayes, random forest, population, vegetation
Subjects: Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 22 Sep 2018 03:56
Last Modified: 22 Sep 2018 03:56

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