Spatial prediction of rotational landslide using geographically weighted regression, logistic regression, and support vector machine models in Xing Guo area (China)

© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This study evaluated the geographically weighted regression (GWR) model for landslide susceptibility mapping in Xing Guo County, China. In this study, 16 conditioning factors, such as slope, aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, normalized difference vegetation index (NDVI), landuse, rainfall, distance to road, distance to river, distance to fault, plan curvature, and profile curvature, were analyzed. Chi-square feature selection method was adopted to compare the significance of each factor with landslide occurence. The GWR model was compared with two well-known models, namely, logistic regression (LR) and support vcector machine (SVM). Results of chi-square feature selection indicated that lithology and slope are the most influencial factors, whereas SPI was found statistically insignificant. Four landslide susceptibility maps were generated by GWR, SGD-LR, SGD-SVM, and SVM models. The GWR model exhibited the highest performance in terms of success rate and prediction accuracy, with values of 0.789 and 0.819, respectively. The SVM model exhibited slightly lower AUC values than that of the GWR model. Validation result of the four models indicates that GWR is a better model than other widely used models

Landslide Mapping and Susceptibility Assessment of Chittagong Hilly Areas, Bangladesh

Landslides are natural phenomena in mountainous areas that cause damage to properties and death to people around the world. In Bangladesh, landslides have caused enormous economic loss and casualty in Chittagong Hilly Areas (CHA). In this dissertation, a landslide inventory of CHA was prepared using Google Earth and field mapping. Google Earth-based mapping helped in recording landslides in inaccessible areas like forests. In contrast, field mapping helped in mapping landslides in accessible areas like areas near road networks. For absence data sampling of landslide susceptibility mapping, this research proposed the Mahalanobis distance (MD) based absence data sampling and compared it with the slope-based absence data sampling. Three Upazilas (subdistricts) of Rangamati district, Bangladesh was used as the study area. Fifteen landslide causal factors, including slope aspect, plan curvature, and geology, were used in the random forest model for landslide susceptibility mapping. The area under the success and prediction rate curves, statistical indices including the Kappa index, showed that both the absence data sampling method provided similar accuracy. But based on the Seed Cell Area Index (SCAI) MD based landslide susceptibility map was more consistent and did not overestimate the landslide susceptibility like the slope-based model. Finally, this study assessed the impact of three land use/land cover (LULC) scenarios: a. existing (2018); b. Proposed LULC (Planned); and c. Simulated (2028) LULC on landslide susceptibility of Rangamati municipality of Rangamati district. The random forest model was used, and it showed that high susceptibility zones would increase in both proposed and simulated LULC scenarios. It indicated that LULC change would increase the landslide susceptibility in the study area. The increase of landslide susceptibility is comparatively low in the proposed LULC, indicating the importance of implementing planned LULC in the study

Landslide susceptibility mapping using machine learning: A literature survey

Landslide is a devastating natural disaster, causing loss of life and property. It is likely to occur more frequently due to increasing urbanization, deforestation, and climate change. Landslide susceptibility mapping is vital to safeguard life and property. This article surveys machine learning (ML) models used for landslide susceptibility mapping to understand the current trend by analyzing published articles based on the ML models, landslide causative factors (LCFs), study location, datasets, evaluation methods, and model performance. Existing literature considered in this comprehensive survey is systematically selected using the ROSES protocol. The trend indicates a growing interest in the field. The choice of LCFs depends on data availability and case study location; China is the most studied location, and area under the receiver operating characteristic curve (AUC) is considered the best evaluation metric. Many ML models have achieved an AUC value > 0.90, indicating high reliability of the susceptibility map generated. This paper also discusses the recently developed hybrid, ensemble, and deep learning (DL) models in landslide susceptibility mapping. Generally, hybrid, ensemble, and DL models outperform conventional ML models. Based on the survey, a few recommendations and future works which may help the new researchers in the field are also presented.Web of Science1413art. no. 302

Remote Sensing Approaches and Related Techniques to Map and Study Landslides

Landslide is one of the costliest and fatal geological hazards, threatening and influencing the socioeconomic conditions in many countries globally. Remote sensing approaches are widely used in landslide studies. Landslide threats can also be investigated through slope stability model, susceptibility mapping, hazard assessment, risk analysis, and other methods. Although it is possible to conduct landslide studies using in-situ observation, it is time-consuming, expensive, and sometimes challenging to collect data at inaccessible terrains. Remote sensing data can be used in landslide monitoring, mapping, hazard prediction and assessment, and other investigations. The primary goal of this chapter is to review the existing remote sensing approaches and techniques used to study landslides and explore the possibilities of potential remote sensing tools that can effectively be used in landslide studies in the future. This chapter also provides critical and comprehensive reviews of landslide studies focus¬ing on the role played by remote sensing data and approaches in landslide hazard assessment. Further, the reviews discuss the application of remotely sensed products for landslide detection, mapping, prediction, and evaluation around the world. This systematic review may contribute to better understanding the extensive use of remotely sensed data and spatial analysis techniques to conduct landslide studies at a range of scales

Landslides

Landslides - Investigation and Monitoring offers a comprehensive overview of recent developments in the field of mass movements and landslide hazards. Chapter authors use in situ measurements, modeling, and remotely sensed data and methods to study landslides. This book provides a thorough overview of the latest efforts by international researchers on landslides and opens new possible research directions for further novel developments

LANDSLIDE SUSCEPTIBILITY MODELLING UNDER ENVIRONMENTAL CHANGES: A CASE STUDY OF CAMERON HIGHLANDS, MALAYSIA

Modeling landslide susceptibility usually does not include multi temporal factors, e.g. rainfall, especially for medium scale. Landslide occurrences in Cameron Highlands, in particular, and in Peninsular Malaysia, in general, tend to increase during the peak times of monsoonal rainfall. Due to the lack of high spatial resolution of rainfall data, Normalized Different Vegetation Index (NDVI), soil wetness, and LST (Land Surface Temperature) were selected as replacement of multi temporal rainfall data. This research investigated their roles in landslide susceptibility modeling. In doing so, four Landsat 7 Enhanced Multi Temporal Plus (ETM+) images acquired during two peak times of rainy and dry seasons were used to derive multi temporal NDVI, soil wetness, and LST. Topographic, geology, and soil maps were used to derive ‘static’ factors namely slope, slope aspect, curvature, elevation, road network, river/lake, lithology, soil geology lineament maps. Landslide map was used to derive weighting system based on spatial relationship between landslide occurrences and landslide factor using bivariate statistical method. A non-statistical weighting system was also used for comparison purpose. Different scenarios of data processing were applied to allow evaluation on the roles of multi temporal factors in landslide susceptibility modeling in terms of the accuracy of the landslide susceptibility maps (LSMs), the appropriate weighting system of the models, the applicability of the model, the ability to confirm the relation between landslide occurrences and rainfall. The results show that the average accuracy of LSMs produced by the developed models with inclusion of multi temporal factors is 49.1% on the overall. Addition of LST tends to improve the accuracy of LSMs. NDVI can be a suitable replacement for rainfall data since it can explain the relation between landslides occurrences and rainfall cycle. Statistical-based weighting system produced more accurate LSMs than non-statistical-based one and is applicable for landslide susceptibility modeling elsewhere. Significant causative factors were proven to produce more accurate LSMs

OPTIMIZING STOCHASTIC SUSCEPTIBILITY MODELLING FOR DEBRIS FLOW LANDSLIDES: MODEL EXPORTATION, STATISTICAL TECHNIQUES COMPARISON AND USE OF REMOTE SENSING DERIVED PREDICTORS. APPLICATIONS TO THE 2009 MESSINA EVENT.

Il presente lavoro di ricerca è stato sviluppato al fine di approfondire approcci metodologici nell'ambito della sucscettibilità da frana. In particolare, il tema centrale della ricerca è rappresentato dal tema specifico dell'esportazione spaziale di modelli di suscettibilità nell'area mediterranea. All'interno del topic specifico dell'esportazione di modelli predittivi spaziali sono state approfondite tematiche relative all'utilizzo di differenti algoritmi o di differenti sorgenti, derivate da DEM o da coperture satellitari.The present work has been developed in order to enhance current methodological approaches within the big picture of the landslide susceptibility. In particular, the central topic was the spatial exportation of landslide susceptibility models within the Mediterranean sector. Within the specific subject pertaining to the spatial exportation of predictive models, different algorithms as well as different data sources have been tested. Data sources experiments assessed the integration of DEM- and remotely sensed- derived parameters in order to improve the landslide prediction