Innovative Landslide Susceptibility Mapping Portrayed by CA-AQD and K-Means Clustering Algorithms

Mao Yimin,Zhang Maosheng,Wang Genglong,Deborah Simon Mwakapesa,Yaser Ahangari Nanehkaran,Li Yican,Muhammad Asim Khan,Lei Weng

doi:10.1155/2021/8846779

Abstract

This study aims at proposing and designing an improved clustering algorithm for assessing landslide susceptibility using an integration of a Chameleon algorithm and an adaptive quadratic distance (CA-AQD algorithm). It targets improving the prediction capacity of clustering algorithms in landslide susceptibility modelling by overcoming the limitations found in present clustering models, including strong dependence on the initial partition, noise, and outliers as well as difficulties in quantifying the triggering factors (such as rainfall/precipitation). The model was implemented in Baota District, Shaanxi province, China. The CA-AQD algorithm was adopted to split all grids in the study area into many groups with more similar characteristic values, which also owed to efficiently quantifying the uncertain (rainfall) value by using AQD. The K-means algorithm divides these groups into five susceptibility classes according to the values of landslide density in each group. The model was then evaluated using statistical metrics and the performance was validated and compared to that of the traditional Chameleon algorithm and KPSO algorithm. The results show that the CA-AQD algorithm attained the best performance in assessing landslide susceptibility in the study area. Thus, this work adds to the literature by introducing the first empirical integration and application of the CA-AQD algorithm to the assessment of landslides in the study area, which then is a new insight to the field. Also, the method can be helpful for dealing with landslides for better social and economic development.

Highlights

Landslides are one of the world’s threatening natural hazards, which are regarded as the part of the masses of rock, compound, or soil that falls down a steep slope [1]
We used the Chameleon adopted adaptive quadratic distance (CA-AQD) algorithms outlined in this study to construct a landslide susceptibility map and validate its performances. e study workflow is shown in Figure 6. e process includes four phases: data collection, clustering analysis, describing the landslide susceptibility map, and model validation
To evaluate the validity of the models, statistical metrics, as well as the area under the ROC curve (AUC)-receiver operating characteristics (ROC) curve, were applied. e results indicated that the CA-AQD and Chameleon models outperformed the K-means particle swarm optimization (KPSO) model in assessing landslide susceptibility in the study area, and more reliable landslide susceptibility maps were produced as they showed AUC values are closer to 1. ese results suggest that both models are good in classifying well the mapping units to their respective clusters. is is due to their ability to perform well in the large study area as well as detecting well the arbitrary shaped and sized clusters, and efficient handling of noise data, which cannot be carried out well by the KPSO model

Summary

Introduction

Landslides are one of the world’s threatening natural hazards, which are regarded as the part of the masses of rock, compound, or soil that falls down a steep slope [1]. Given that appropriate soils and rocks engineering data, slope geometry, discontinuity features, and hydrological factors are required to compute the resisting and driving forces association, deterministic models have been limited to small study areas [9, 10] Statistical models, such as linear and logistic regression [11,12,13], bivariate statistical models [14,15,16], frequency ratio [17,18,19,20,21], and weight of evidence models [22,23,24], have been applied widely to the field of constructing assessment models for landslide susceptibility. Statistical models, such as linear and logistic regression [11,12,13], bivariate statistical models [14,15,16], frequency ratio [17,18,19,20,21], and weight of evidence models [22,23,24], have been applied widely to the field of constructing assessment models for landslide susceptibility. ese models, cannot determine the relationship between significant landslide-influencing factors and complicated landslide systems [25]

Objectives

Methods

Results

Conclusion