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Comparative landslide spatial research based on various sample sizes and ratios in Penang Island, Malaysia

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Abstract

This paper aims to compare and develop the influence on different sample sizes and sample ratios when using machine learning (ML) models, i.e., support vector machine (SVM) and artificial neural network (ANN), to produce landslide susceptibility maps (LSMs) in Penang Island, Malaysia. At the same time, traditional statistical (TS) models are also considered to produce LSMs in this comparative research. The receiver operating characteristic (ROC) curve and recall metric are applied to evaluate the model’s performance. Based on the evaluation criteria, the ML model outperforms the TS models and the ML models trained using the datasets with larger sample size give a better performance. ML models, especially SVM models, have better performance when training with balanced datasets as well as the datasets of more landslide sample data. Kruskal-Wallis test and Mann-Whitney U test are applied to test the significance. The results indicate that sample size and sample ratio are essential factors when considering ML models to produce LSMs. The LSMs produced in this research can provide valid and useful information to the local authorities for landslide mitigation and prediction.

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Funding

This study was funded by RUI Grant (1001/PMATHS/8011093) from Universiti Sains Malaysia.

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Authors and Affiliations

  1. School of Mathematical Sciences, Universiti Sains Malaysia, 11800, USM, Penang, Malaysia

    Han Gao & Pei Shan Fam

  2. School of Electrical and Electronic Engineering, USM, Engineering Campus, Seberang Perai Selatan, 14300, Nibong Tebal, Penang, Malaysia

    Lea Tien Tay

  3. Research and Innovation Unit, Universiti Sains Malaysia, 11800, USM, Penang, Malaysia

    Heng Chin Low

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  1. Han Gao
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  2. Pei Shan Fam
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  4. Heng Chin Low
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Correspondence to Pei Shan Fam.

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Gao, H., Fam, P.S., Tay, L.T. et al. Comparative landslide spatial research based on various sample sizes and ratios in Penang Island, Malaysia. Bull Eng Geol Environ 80, 851–872 (2021). https://doi.org/10.1007/s10064-020-01969-7

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