Landslide Detection Based on Bayesian Classification Method
Pushparaj. D1, Uma Priyadarsini2
1Pushparaj. D, UG Scholar, Assistant Professor, Saveetha of Engineering, (Tamil Nadu), India.
2Uma Priyadarsini, Department of Computer Science and Engineering, Saveetha of Engineering, (Tamil Nadu), India.
Manuscript received on 20 July 2019 | Revised Manuscript received on 03 August 2019 | Manuscript Published on 10 August 2019 | PP: 793-795 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B11470782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1147.0782S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The study of landslide is a very difficult task due to high space temporal variety of involved parameters. The study of munnar city landslide has been performed by a data mining method called Bayesian classification. The dataset related to detect the landslide were soil and moisture parameters. These data sets are the basis of this work. The cumulative pattern related to the landslides depends on the data accumulated from the various sensors like geophysical sensor and moisture, soil sensor.
Keywords: Landslide, Bayesian Classification, Rainfall Rate, Geographical Sensor, Moisture Sensor.
Scope of the Article: Classification