Landslide Type Prediction using Random Forest Classifier

Authors

  • Harish Kumar NG Dept. of Information Technology, PSG College of Technology, Coimbatore – 641 004, India
  • Pooventhiran G Dept. of Information Technology, PSG College of Technology, Coimbatore – 641 004, India
  • Karthika Renuka D Dept. of Information Technology, PSG College of Technology, Coimbatore – 641 004, India

DOI:

https://doi.org/10.26438/ijcse/v8i2.711

Keywords:

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

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.

References

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Published

2020-02-28
CITATION
DOI: 10.26438/ijcse/v8i2.711
Published: 2020-02-28

How to Cite

[1]
H. K. NG, P. G, and K. R. D, “Landslide Type Prediction using Random Forest Classifier”, Int. J. Comp. Sci. Eng., vol. 8, no. 2, pp. 7–11, Feb. 2020.

Issue

Vol. 8 No. 2 (2020): IJCSE February Edition

Section

Research Article

License

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