Improving Voting Feature Intervals for Spatial Prediction of Landslides

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.

Landslide Susceptibility Mapping Using Single Machine Learning Models: A Case Study from Pithoragarh District, India

“…One of the important issues in the process of spatial modeling of landslide susceptibility is the selection of the landslide influencing factors and the preparation of the thematic maps of the features. ere is no uniform rule for the selection of landslides influencing factors [60,61]; thereby these factors are selected based on previous similar studies and knowledge of the topography, geology, and geoenvironmental conditions of the study area [62,63]. Anthropogenic activities such as the construction, infrastructure development, and land use changes can disturb the natural stability of the ground mass causing landslides in the area.…”

“…Elevation affects local climate and air/ weather conditions of regions [70] and is an important factor for describing the other factors, such as landform, rainfall, land use type, human activities, vegetation type and distribution, and soil weathering conditions as well as the rate of erosion which affects landslides [71,72]. Generally, rainfall and weathering occur at lower to moderate elevations.…”

Assessment of Ensemble Models for Groundwater Potential Modeling and Prediction in a Karst Watershed

“…Choosing a suitable method for assessing water infiltration is often controversial. Geographic information systems (GISs) are powerful tools for classifying, analyzing, and retrieving information, and have been used to replace exploration and spatial experimentation in the field [12][13][14]. Saving time and money, the ability to perform complex spatial and non-spatial data analyses, and flexibility are features of GISs and these have made them attractive for groundwater study [15,16].…”