Improving Voting Feature Intervals for Spatial Prediction of Landslides

Binh Thai Pham,Mohammadtaghi Avand,Indra Prakash,Hiep Van Le,Sushant K Singh,Nadhir Al-Ansari,Tran Van Phong

doi:10.1155/2020/4310791

Abstract

In this study, the main aim is to improve performance of the voting feature intervals (VFIs), which is one of the most effective machine learning models, using two robust ensemble techniques, namely, AdaBoost and MultiBoost for landslide susceptibility assessment and prediction. For this, two hybrid models, namely, AdaBoost-based Voting Feature Intervals (ABVFIs) and MultiBoost-based Voting Feature Intervals (MBVFIs) were developed and validated using landslide data collected from one of the landslide affected districts of Vietnam, namely, Muong Lay. Quantitative validation methods including area under the ROC curve (AUC) were used to evaluate model performance. The results indicated that both the newly developed ensemble models ABVFI (AUC = 0.859) and MBVFI (AUC = 0.839) outperformed the single VFI (AUC = 0.824) model. Thus, ensemble framework-based VFI algorithms can be used for the accurate spatial prediction of landslides, which can also be applied in other landslide prone regions of the world. Landslide susceptibility maps developed by ensemble VFI models can be used for better landslide prevention and risk management of the area.

Highlights

In recent years, population growth and development in unstable hilly areas have led to an increase in natural disasters such as landslides [1]
Muong Lay district, which is one of the most landslide affected areas of Vietnam, was selected as the study area. e main contribution of this study is in the development and application of a novel hybrid approach for accurate landslide susceptibility mapping. Validation of these models was carried out using different quantitative statistical indices including area under the receiver operating characteristic (ROC) curve and accuracy
Study Area e study area of Muong Lay district is located in the northwest of Vietnam between 22°0′N and 22°5′N and 103°5′E and 103°10′E, covering 11403 km2 is highly prone to landslides (Figure 1). e area is located, at the confluence of Da, Nam Na, and Nam Lay Rivers in a narrow and long valley [3, 4]. e elevation varies between 125 and 1778 m

Summary

Introduction

Population growth and development in unstable hilly areas have led to an increase in natural disasters such as landslides [1]. Ese concerned led to the use of machine learning (ML) and data mining techniques in landslide studies [1, 26] Nowadays, these methods are being used more widely for landslide susceptibility mapping due to their accuracy and speed [13, 14, 27]. Erefore, there is always scope of improvement in methodology by using different combinations of algorithms With this objective, a new ensemble framework-based ML models, namely, ABVFI and MBVFI, which are combination of a popular single ML model voting feature intervals (VFIs), and two effective ensemble techniques, namely, AdaBoost and MultiBoost algorithms, were proposed for the development of landslide susceptibility maps. E main contribution of this study is in the development and application of a novel hybrid approach for accurate landslide susceptibility mapping Validation of these models was carried out using different quantitative statistical indices including area under the ROC curve and accuracy. Weka and ArcGIS software were implemented for processing the data, modeling, and mapping of landslide susceptibility

Methods

Geospatial Database

Modeling Methodology

Results and Analysis

Discussion

Very low Landslide susceptibility analysis