The present study aims to develop two hybrid models to optimize the factors and enhance the predictive ability of the landslide susceptibility models. For this, a landslide inventory map was created with 406 historical landslides and 2030 non-landslide points, which was randomly divided into two datasets for model training (70%) and model testing (30%). 22 factors were initially selected to establish a landslide factor database. We applied the GeoDetector and recursive feature elimination method (RFE) to address factor optimization to reduce information redundancy and collinearity in the data. Thereafter, the frequency ratio method, multicollinearity test, and interactive detector were used to analyze and evaluate the optimized factors. Subsequently, the random forest (RF) model was used to create a landslide susceptibility map with original and optimized factors. The resultant hybrid models GeoDetector-RF and RFE-RF were evaluated and compared by the area under the receiver operating characteristic curve (AUC) and accuracy. The accuracy of the two hybrid models (0.868 for GeoDetector-RF and 0.869 for RFE-RF) were higher than that of the RF model (0.860), indicating that the hybrid models with factor optimization have high reliability and predictability. Both RFE-RF GeoDetector-RF had higher AUC values, respectively 0.863 and 0.860, than RF (0.853). These results confirm the ability of factor optimization methods to improve the performance of landslide susceptibility models.

Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization

This study investigates how effective the receiver operating characteristic (ROC) curve is for comparing the reliability of landslide susceptibility maps (LSMs). In this regard, we selected a basin...

https://www.tandfonline.com/doi/pdf/10.1080/19475705.2018.1424043

Is the ROC curve a reliable tool to compare the validity of landslide susceptibility maps

The objective of this study was to identify the areas that are most susceptible to landslide occurrence, and to find the key factors associated with landslides along Jinsha River and its tributaries close to Derong and Deqin County. Thirteen influencing factors, including (a) lithology, (b) slope angle, (c) slope aspect, (d) TWI, (e) curvature, (f) SPI, (g) STI, (h) topographic relief, (i) rainfall, (j) vegetation, (k) NDVI, (l) distance-to-river, (m) and distance-to-fault, were selected as the landslide conditioning factors in landslide susceptibility mapping. These factors were mainly obtained from the field survey, digital elevation model (DEM), and Landsat 4–5 imagery using ArcGIS software. A total of 40 landslides were identified in the study area from field survey and aerial photos’ interpretation. First, the frequency ratio (FR) method was used to clarify the relationship between the landslide occurrence and the influencing factors. Then, the principal component analysis (PCA) was used to eliminate multiple collinearities between the 13 influencing factors and to reduce the dimension of the influencing factors. Subsequently, the factors that were reselected using the PCA were introduced into the logistic regression analysis to produce the landslide susceptibility map. Finally, the receiver operating characteristic (ROC) curve was used to evaluate the accuracy of the logistic regression analysis model. The landslide susceptibility map was divided into the following five classes: very low, low, moderate, high, and very high. The results showed that the ratios of the areas of the five susceptibility classes were 23.14%, 22.49%, 18.00%, 19.08%, and 17.28%, respectively. And the prediction accuracy of the model was 83.4%. The results were also compared with the FR method (79.9%) and the AHP method (76.9%), which meant that the susceptibility model was reasonable. Finally, the key factors of the landslide occurrence were determined based on the above results. Consequently, this study could serve as an effective guide for further land use planning and for the implementation of development.

Landslide Susceptibility Mapping Using Logistic Regression Analysis along the Jinsha River and Its Tributaries Close to Derong and Deqin County, Southwestern China

Landslides lead to a great threat to human life and property safety. The delineation of landslide-prone areas achieved by landslide susceptibility assessment plays an important role in landslide management strategy. Selecting an appropriate mapping unit is vital for landslide susceptibility assessment. This paper compares the slope unit and grid cell as mapping unit for landslide susceptibility assessment. Grid cells can be easily obtained and their matrix format is convenient for calculation. A slope unit is considered as the watershed defined by ridge lines and valley lines based on hydrological theory and slope units are more associated with the actual geological environment. Using 70% landslide events as the training data and the remaining landslide events for verification, landslide susceptibility maps based on slope units and grid cells were obtained respectively using a modified information value model. ROC curve was utilized to evaluate the landslide susceptibility maps by calculating the training accuracy and predictive accuracy. The training accuracies of the grid cell-based susceptibility assessment result and slope unit-based susceptibility assessment result were 80.9 and 83.2%, and the prediction accuracies were 80.3 and 82.6%, respectively. Therefore, landslide susceptibility mapping based on slope units performed better than grid cell-based method.

A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment

The determination of mapping units, including grid, slope, unique condition, administrative division, and watershed units, is a very important modeling basis for landslide assessments. Among these mapping units, the slope unit has been paid a lot of attention because it can effectively reflect the physical relationships between landslides and the fundamental topographic elements especially in mountainous areas. Although some methods have been proposed for slope unit extraction, effectively and automatically extracting slope units remains a difficult and urgent problem that seriously restricts the use of slope units. To overcome this problem, the innovative multi-scale segmentation (MSS) method is proposed for extracting slope units. Thus, first, the terrain aspect and shaded relief images obtained from the digital elevation model with certain weights are used as the data sources of the MSS method. Second, the scale, shape, and compactness parameters of the MSS method are properly determined according to the improved trial-and-error method. Third, the initial slope units generated by the MSS method with appropriate parameters are automatically optimized through vector analysis in GIS. Finally, reasonable slope units are obtained and the extraction performance is discussed. The Chongyi County and Wanzhou District in China are selected as study areas. The conventional hydrological method is also adopted to extract slope units for qualitative and quantitative comparisons. It can be concluded that the MSS method can accurately and automatically extract the slope units for landslide assessments in hilly and mountainous areas and performs better than the hydrological method.

Efficient and automatic extraction of slope units based on multi-scale segmentation method for landslide assessments

The Longzi River Basin in Tibet is located along the edge of the Himalaya Mountains and is characterized by complex geological conditions and numerous landslides. To evaluate the susceptibility of landslide disasters in this area, eight basic factors were analyzed comprehensively in order to obtain a final susceptibility map. The eight factors are the slope angle, slope aspect, plan curvature, distance-to-fault, distance-to-river, topographic relief, annual precipitation, and lithology. Except for the rainfall factor, which was extracted from the grid cell, all the factors were extracted and classified by the slope unit, which is the basic unit in geological disaster development. The eight factors were superimposed using the information content method (ICM), and the weight of each factor was acquired through an analytic hierarchy process (AHP). The sensitivities of the landslides were divided into four categories: low, moderate, high, and very high, respectively, accounting for 22.76%, 38.64%, 27.51%, and 11.09% of the study area. The accuracies of the area under AUC using slope units and grid cells are 82.6% and 84.2%, respectively, and it means that the two methods are accurate in predicting landslide occurrence. The results show that the high and very high susceptibility areas are distributed throughout the vicinity of the river, with a large component in the north as well as a small portion in the middle and the south. Therefore, it is necessary to conduct landslide warnings in these areas, where the rivers are vast and the population is dense. The susceptibility map can reflect the comprehensive risk of each slope unit, which provides an important reference for later detailed investigations, including research and warning studies.

https://www.mdpi.com/2220-9964/6/6/172/pdf

Peihua Xu

Papers

Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping along the Longzi River, Southeastern Tibetan Plateau, China