Spatio-statistical comparative approaches for landslide susceptibility modeling: case of Mae Phun, Uttaradit Province, Thailand

Abstract

In this study for landslide susceptibility modeling, three quantitative techniques, i.e., frequency ratio (FR), information value (IFV), and weight of evidence (WOE), were evaluated and compared. For this purpose, landslide inventory map was prepared using visual interpretation on SPOT-5 image and field survey was carried out for ground truthing of landslide sites and total 677 landslides were identified. The inventory map was divided into training and validation datasets, and from total, 473 landslides (70%) were for training to run the model and 30% (204 landslides) for validation purpose. Total 11 landslide conditioning factors were used in this study that are: elevation, slope, aspect, curvature, plan curvature, profile curvature, land use/land cover (LULC), topographic wetness index (TWI), stream power index (SPI), proximity to road, and proximity to stream. Three different landslide susceptibility maps were produced based on analyzing the relationship of landslides with conditioning factors using FR, IFV, and WOE in GIS environment. The results of FR model indicated that almost 40% of the total study area fall in high to very high landslide susceptibility zones, while in WOE and IFV models, it was found almost 50% of the total area. The landslide susceptibility maps were validated using prediction and success rate curve techniques. The prediction rate curve gives us a glimpse of future landslides based on present landslide susceptibility maps. The results obtained from validation showed that the area under curve (AUC) based on prediction rate curve for FR, IFV, and WOE was 80.78%, 72.88%, and 72.33%, respectively. However, the AUC obtained through success rate curve for the models in this study was 74.60%, 75.04%, and 72.54% for FR, IFV, and WOE, respectively. Moreover, the evaluation of landslide density test and seed cell index area (SCAI) indicated that calculated and classified landslide susceptibility maps are in a good agreement with the field conditions. Thus, it was observed from this study that the frequency ratio has better accuracy as compared to information value and weight of evidence, but in success rate curve, almost all the models showed the same results. Consequently, it can be concluded that the susceptibility maps produced from FR, IFV, and WOE are in good agreement because more than two-third of landslides falls in high and very high susceptibility zones of each model. From this study, it was found that slope angle, elevation, land use/ land cover, and roads play a major influencing role in the occurrence of landslide in the study area. The maps produced based on these landslide susceptibility models provide a base for engineers and land use planner to develop landslide management strategies before any development on slopes.

Comparison of Statistical and Machine-Learning Model for Analyzing Landslide Susceptibility in Sumedang Area, Indonesia

“…The slope will also affect soil strength and, in some instances, lead to landslide potential [5], with higher slope values (especially those with slopes that are greater than 45%) causing rainwater to potentially erode the soil (thus, resulting in potential landslides). In accordance with [4] and [46], the degree of the slope of an area shows a strong relationship with landslides. Some types of soil that easily absorb water and with a certain thickness also have the potential to cause landslides.…”

“…This phenomenon has also happened in many other places in the world, and it has caused environmental harm and land degradation in addition to losses in terms of people and money [3]. Moreover, landslides are terrible natural disasters every year in mountainous regions across the world that result in significant injuries to people, fatalities, and significant property destruction [4]. Although the physical causes of landslides cannot be eliminated through geological examinations, engineering, nor land-use management for reducing landslide hazards, it is essential to understand their potential exposure.…”

Assessing landslide susceptibility in Lake Abya catchment, Rift Valley, Ethiopia: A GIS-based frequency ratio analysis

“…This data can be gathered using remote sensing images, GIS tools, and on-site surveys (Chandra and Indrajit, 2019;Rahman and Collins, 2017;Hamid et al, 2023). A landslide inventory map is employed to ascertain the association that exists between landslides and the relevant environmental variables (Shrestha and Poudel, 2018;Gazibara et al, 2019;Moazzam et al, 2020). A map has been generated illustrating the locations of 138 known landslides.…”

“…Combining GIS with the frequency ratio method makes it possible to identify landslide-prone areas more effectively, accurately, and efficiently. As a result of this integrated approach, detailed information essential to land-use planning and disaster risk reduction programs can be made more accessible (Rahman and Collins, 2017;Moazzam et al, 2020;Huang et al, 2023). Furthermore, this research holds the potential to significantly advance the understanding of landslide dynamics in the region.…”

Geographic Object-Based Image Analysis (GEOBIA) for Landslide Identification using Machine Learning on Google Earth Engine (GEE)

“…There is an inverse relationship between Elevation and rainfall; as the Elevation increases, the rainfall intensity decreases, and vice versa. The high rainfall on fragile slopes leads to high chances of landslides (Moazzam et al 2020). So, the frequency of landslides increases with the increase in Elevation to a certain extent (Dai et al 2001).…”