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Landslide Type Prediction using Random Forest Classifier

Harish Kumar N.G.1 , Pooventhiran G.2 , Karthika Renuka D.3

Section:Research Paper, Product Type: Journal Paper
Volume-8 , Issue-2 , Page no. 7-11, Feb-2020

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v8i2.711

Online published on Feb 28, 2020

Copyright © Harish Kumar N.G., Pooventhiran G., Karthika Renuka D. . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Harish Kumar N.G., Pooventhiran G., Karthika Renuka D., “Landslide Type Prediction using Random Forest Classifier,” International Journal of Computer Sciences and Engineering, Vol.8, Issue.2, pp.7-11, 2020.

MLA Style Citation: Harish Kumar N.G., Pooventhiran G., Karthika Renuka D. "Landslide Type Prediction using Random Forest Classifier." International Journal of Computer Sciences and Engineering 8.2 (2020): 7-11.

APA Style Citation: Harish Kumar N.G., Pooventhiran G., Karthika Renuka D., (2020). Landslide Type Prediction using Random Forest Classifier. International Journal of Computer Sciences and Engineering, 8(2), 7-11.

BibTex Style Citation:
@article{N.G._2020,
author = {Harish Kumar N.G., Pooventhiran G., Karthika Renuka D.},
title = {Landslide Type Prediction using Random Forest Classifier},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {2 2020},
volume = {8},
Issue = {2},
month = {2},
year = {2020},
issn = {2347-2693},
pages = {7-11},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5022},
doi = {https://doi.org/10.26438/ijcse/v8i2.711}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v8i2.711}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5022
TI - Landslide Type Prediction using Random Forest Classifier
T2 - International Journal of Computer Sciences and Engineering
AU - Harish Kumar N.G., Pooventhiran G., Karthika Renuka D.
PY - 2020
DA - 2020/02/28
PB - IJCSE, Indore, INDIA
SP - 7-11
IS - 2
VL - 8
SN - 2347-2693
ER -

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Abstract

This paper talks about the prediction of types of landslides. It employs Random Forest Classifier technique, the ensemble version of Decision Trees. The results of the experiment show that ensemble techniques provide a better result compared to other algorithms. The dataset used here, in this paper, is Landslides After Rainfall dataset from NASA. This model achieves 59% accuracy without feature selection and 84% accuracy with feature selection.

Key-Words / Index Term

Artificial Intelligence, Machine Learning, Decision Tree, Ensemble Learning, Random Forest Classifier

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