UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Volume 11 | Issue 11 | November 2024

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Volume 11 Issue 1
January-2024
eISSN: 2349-5162

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Published Paper ID:
JETIR2401568


Registration ID:
532013

Page Number

f564-f570

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Title

Comparative Analysis of Slope Stability Prediction using Logistic Regression and Random Forest Approach

Abstract

The study presents a comparative analysis of three prediction models, Logistic Regression (LR), Random Forest (RF), and Multi-Layer Perceptron Neural Networks (MLPNN). It employs a trial and error method for optimizing hyperparameters in slope stability prediction. The study utilizes a dataset comprising 108 slope cases with various influencing factors, including unit weight (Ƴ), cohesion (c), angle of internal friction (ɸ), slope angle (β), height (H), pore water pressure coefficient (ru), and factor of safety (FS) as inputs, while slope status (S) serves as the output variable. Based on the confusion matrix and ROC curves, the RF model demonstrated superior performance over the other models. The utilization of RF enhances the capacity and efficiency of slope deformation prediction models, establishing it as the most accurate tool for forecasting slope stability.

Key Words

Machine Learning, Slope Stability, Limit Equilibrium, Logistic Regression, Neural Networks, Artificial Intelligence

Cite This Article

"Comparative Analysis of Slope Stability Prediction using Logistic Regression and Random Forest Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.f564-f570, January-2024, Available :http://www.jetir.org/papers/JETIR2401568.pdf

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2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Comparative Analysis of Slope Stability Prediction using Logistic Regression and Random Forest Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. ppf564-f570, January-2024, Available at : http://www.jetir.org/papers/JETIR2401568.pdf

Publication Details

Published Paper ID: JETIR2401568
Registration ID: 532013
Published In: Volume 11 | Issue 1 | Year January-2024
DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.37710
Page No: f564-f570
Country: Bhopal, Madhya Pradesh, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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