Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside Susceptibility Assessment at Pauri Garhwal Area, Uttarakhand, India

Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside... Landslide susceptibility assessment has been conducted at the Pauri Garhwal area of Uttarakhand state, India, an area affected by numerous landslides causing significant losses of life, infrastructure and property every year. Decision tree-based machine learning methods, namely Random Forest (RF), Logistic Model Trees (LMT), Best First Decision Trees (BFDT) and Classification and Regression Trees (CART) have been used, and results are compared herein for proper spatial prediction of landslides. Analysis of the data has been done considering sixteen conditioning factors (i.e., slope angle, elevation, slope aspect, profile curvature, land cover, curvature, lithology, plan curvature, soil, distance to lineaments, lineament density, distance to roads, road density, distance to river, river density and rainfall), and 1295 historical landslide polygons. Models were validated and compared using Receiver Operating Characteristics (ROC) curve and statistical indices. The results show that the RF model has the highest predictive capability, followed by the LMT, BFDT and CART models, respectively, and indicate that although all four methods have shown good results, the performance of the RF method is the best for landslide spatial prediction. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Environmental Processes Springer Journals

Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside Susceptibility Assessment at Pauri Garhwal Area, Uttarakhand, India

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Publisher
Springer International Publishing
Copyright
Copyright © 2017 by Springer International Publishing AG
Subject
Earth Sciences; Environmental Science and Engineering; Environmental Management; Waste Management/Waste Technology; Water Quality/Water Pollution
ISSN
2198-7491
eISSN
2198-7505
D.O.I.
10.1007/s40710-017-0248-5
Publisher site
See Article on Publisher Site

Abstract

Landslide susceptibility assessment has been conducted at the Pauri Garhwal area of Uttarakhand state, India, an area affected by numerous landslides causing significant losses of life, infrastructure and property every year. Decision tree-based machine learning methods, namely Random Forest (RF), Logistic Model Trees (LMT), Best First Decision Trees (BFDT) and Classification and Regression Trees (CART) have been used, and results are compared herein for proper spatial prediction of landslides. Analysis of the data has been done considering sixteen conditioning factors (i.e., slope angle, elevation, slope aspect, profile curvature, land cover, curvature, lithology, plan curvature, soil, distance to lineaments, lineament density, distance to roads, road density, distance to river, river density and rainfall), and 1295 historical landslide polygons. Models were validated and compared using Receiver Operating Characteristics (ROC) curve and statistical indices. The results show that the RF model has the highest predictive capability, followed by the LMT, BFDT and CART models, respectively, and indicate that although all four methods have shown good results, the performance of the RF method is the best for landslide spatial prediction.

Journal

Environmental ProcessesSpringer Journals

Published: Jun 23, 2017

References

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