Machine learning and deep learning-based landslide susceptibility mapping using geospatial techniques in Wayanad, Kerala state, India

Landslide susceptibility maps are widely used in landslide hazard management. Although many models have been proposed, mapping unit definition is a matter that still needs to be fully examined. In the literature, the most reported mapping units are pixels and slope units, while in this work, developed in the Rio de Janeiro region (Brazil), the use of drainage basins as a mapping unit is examined; even if their use leads to the definition of maps with a coarser spatial resolution than pixels-based maps, they convey information that can be easily and rapidly handled by civil defense organizations. However, for the morphometrical characterization of entire basins, a standardized procedure does not exist, and the susceptibility results may be sensitive to the approach used. To investigate this issue, a random forest model was used to assess landslide susceptibility, using 12 independent variables: four categorical (land use, soil type, lithology and slope orientation) and eight numerical variables (slope gradient, elevation, slope curvature, profile curvature, planar curvature, flow accumulation, topographic wetness index, stream power index). For each basin, the numerical variables were aggregated according to different approaches, which, in turn, were used to set up four different model configurations: i) maximum values, ii) mean values, iii) standard deviation values, iv) joint use of all the above. The resulting maps showed noticeable differences and a quantitative validation procedure showed that the best configurations were the ones based on mean values of independent variables, and the one based on the combination of all the values of the numerical variables. The main outcomes of this work consist of a landslide susceptibility map of the study area, to be used in operational procedures of risk management and in some insights on the best approaches to aggregate raster cell data into wider spatial units.

GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models

The spatial prediction of landslide susceptibility locations is a crucial task to support risk management and development plans in mountainous areas, such as El-Qaa area. The study aims to delineate landslide-susceptible zones that can cause enormous damage to property, infrastructure, and loss of life. An innovative integrated approach using remote sensing, geographic information systems, and geophysical techniques was used in the current work to evaluate landslide susceptibility locations. Magnetic data were supported by information derived from geologic, geomorphologic, topographic, and seismic data to reveal the landslides-prone zones. Several factors contributing to landslide susceptibility in El-Qaa area were determined, such as distance to faults, lithology, stream power index, slope, density of earthquake events, distance to epicenters, tilt derivative of magnetic data, distance to drainages, aspect, and topographic wetness index. A unique landslide susceptibility model (LSM) was developed in this study by integration all the spatial data that represent the contributing factors. The bivariate statistical index method was constructed to assign logic ranks and weights for the causative factors and their classes representing their realistic relations with landslide susceptibility in El-Qaa area. The landslide susceptibility map classifies El-Qaa area into five relative susceptibility zones: very high, high, moderate, low, and very low. The very high- and high-susceptibility zones are distributed in the eastern side of El-Qaa area where structurally controlled channels, steep topography to downhill lands, and Precambrian basement rocks are located. The resulting susceptibility map was tested and validated using the landslide locations that were delineated from field survey and satellite images at high resolution. The integrated methodology shows a more realistic landslide susceptibility map and adds a powerful tool to design a fruitful management plan in mountainous areas.

The present study is dealt with the application of weighted overlay analysis (WOA) model, certainty factor (CF) model, analytical hierarchy process (AHP) model for the preparation of landslide susceptibility zonation map of Darjeeling Himalaya. To perform three models, various data layers with regard to elevation, slope aspect, slope angle, slope curvature, geology, soil, lineament density, distance to lineament, drainage density, distance to drainage, stream power index (SPI), topographic wetted index (TWI), rainfall, normalized differential vegetation index (NDVI) and land use and land cover (LULC) were taken into account. For the preparation of various data layers, topographical maps, Google earth images, SRTM DEM, http://www.worldclim.org, satellite image (Landsat TM) and some authorized data were being processed on GIS environment (ArcMap 10.1). The prepared landslide susceptibility maps of Darjeeling Himalaya were classified into five, i.e. very low, low, moderate, high, and very high landslide susceptibility. To validate three landslide susceptibility zonation maps derived from WOA, CF, and AHP models, ROC Curve and frequency ratio plot methods were incorporated. ROC curve showed the level of accuracy of each landslide susceptibility map. The study revealed that WOA, CF, and AHP were with the accuracy level of 65.4%, 81.2%, and 67.5%. Frequency ratio plots sugessted that moderate, high, and very high landslide susceptibility zones in Darjeeling Himalaya are experienced with greater probability landslide phenomena.

The main objective of this study is to investigate potential application of frequency ratio (FR), weights of evidence (WoE), and statistical index (SI) models for landslide susceptibility mapping in a part of Mazandaran Province, Iran. First, a landslide inventory map was constructed from various sources. The landslide inventory map was then randomly divided in a ratio of 70/30 for training and validation of the models, respectively. Second, 13 landslide conditioning factors including slope degree, slope aspect, altitude, plan curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, lithology, distance from streams, faults, roads, and land use type were prepared, and the relationships between these factors and the landslide inventory map were extracted by using the mentioned models. Subsequently, the multi-class weighted factors were used to generate landslide susceptibility maps. Finally, the susceptibility maps were verified and compared using several methods including receiver operating characteristic curve with the areas under the curve (AUC), landslide density, and spatially agreed area analyses. The success rate curve showed that the AUC for FR, WoE, and SI models was 81.51, 79.43, and 81.27, respectively. The prediction rate curve demonstrated that the AUC achieved by the three models was 80.44, 77.94, and 79.55, respectively. Although the sensitivity analysis using the FR model revealed that the modeling process was sensitive to input factors, the accuracy results suggest that the three models used in this study can be effective approaches for landslide susceptibility mapping in Mazandaran Province, and the resultant susceptibility maps are trustworthy for hazard mitigation strategies.

Chapter 9 - Landslide susceptibility modeling using a generalized linear model in a tropical river basin of the Southern Western Ghats, India

In the mountainous area of southwest China, there is a large number of large hydropower stations that have already been built or under construction. The construction and the safety of the operation of hydropower stations directly affect the residents’ safety of life and property on both sides of the downstream river. Landslide hazards are one of the main problems affecting the construction and operation of hydropower stations. Therefore, it is currently more urgent to evaluate landslide susceptibility within the basin. The goal of this paper is to carry out landslide susceptibility mapping that affects the construction and operation of hydropower stations. In the case of a large research area and lack of landslide hazard data, this paper selects the sample of blocking river landslide in a larger area with similar topography and geological conditions and applies the quantitative method to carry out landslide sensitivity mapping. In this paper, the slope unit works as the evaluation unit, 191 landslide sample data were obtained, and nine landslide conditioning factors (slope, aspect, curvature, lithology, distance to fault, distance to the river, land use, soil, and the topographic wetness index (TWI)) were obtained. Combined with the quantitative frequency model (FR model), the landslide susceptibility map was obtained. The research process and results show that (1) when the study area is large, the landslide data is scarce, and it is difficult to get the landslide inventory map; the landslide samples can be selected in a larger area with similar topography and geological conditions. It is feasible to use the analysis data of landslide samples to evaluate the landslide susceptibility; (2) for specific industry applications such as hydropower station operation safety, the landslide evaluation range can be reduced by the spatial correlation between landslide aspect and river flow direction, which is beneficial to the management department to reduce the scope of attention; and (3) the verification results show that the slope unit can be used for quantitative evaluation of landslide sensitivity based on the FR model and can obtain the highest evaluation accuracy. The main advantage of this method is its capability to carry out landslide susceptibility mapping with quantitative methods and to ensure that higher mapping accuracy is obtained in areas where landslide hazard data is lacking.

Intelligent data analytics approaches are popular in landslide susceptibility mapping. This chapter develops a random forest (RF) approach for spatial modeling of landslide susceptibility. A total number of 78 landslide locations are identified using field survey, 55 of which are randomly selected to model landslide susceptibility and remaining 23 locations considered for model validation. Twelve predictor variables are selected: elevation, slope percentage, slope aspect, plan curvature, profile curvature, distance from roads, distance from streams, distance from faults, lithological formations, land use, soil type, and topographic wetness index (TWI) to create an RF model for landslide susceptibility mapping. The results of RF model are evaluated using efficiency (E), true positive rate (TPR), false positive rate (FPR), true skill statistic (TSS), and area under receiver operating characteristic curve (AUC) in training and validation steps. RF model registered excellent goodness-of-fit with AUC = 93.6%, E = 0.887, TSS = 0.776, TPR = 0.905, FPR = 0.129, and predictive performance with AUC = 90.7%, E = 0.777, TSS = 0.559, TPR = 0.809, FPR = 0.25. Intelligent data analytic method, therefore, has a significant promise in tackling challenges of landslide susceptibility mapping in large regions, which may not have sufficient geotechnical data to employ a physically based method.

Landslide susceptibility and building exposure assessment using machine learning models and geospatial analysis techniques

This research aimed to assess landslide susceptibility in the Badakhshan province of Afghanistan, an area highly susceptible to landslides due to its complex topography and geological conditions. Three distinct machine learning (ML) models, namely the Generalized Linear Model (GLM), Maximum Entropy (ME), and Random Forest (RF), were employed to identify the key contributing factors to landslide occurrences in the study region. The dataset used in this study consisted of landslide conditioning factors and a landslide inventory map. The conditioning factors encompassed lithology, soil type, plane curvature, profile curvature, elevation, slope, aspect, precipitation, land use/land cover (LULC), distance to fault, river, road, Normalized Difference Vegetation Index (NDVI), Topographic Wetness Index (TWI), Terrain Ruggedness Index (TRI), and Standardized Precipitation Index (SPI). The landslide inventory map contained 177 landslide locations and 65 non-landslide points obtained from Google Earth. Each machine learning (ML) model was trained and implemented independently using 70% of the training data, with the results validated against the remaining 30% of the landslide inventory dataset. Ensemble results from GLM, ME, and RF were obtained using the median approach. All three models exhibited consistent performance and identified similar landslide-prone areas. Among the various factors studied, proximity to rivers emerged as the most influential factor contributing to landslides, followed by the distance to roads and slope gradient. The study revealed that the districts of Argo and Yaftali Sufla, near Faizabad, were identified as particularly susceptible to landslides, especially in the vicinity of large valleys. Out of the total study area of 3086.4 km2, ∼2162 km2 were deemed relatively safe from landslides, while 149 km2 (representing 4.8% of the study region) were identified as highly susceptible to landslides. Area Under the Receiver Operating Characteristic Curve (AUC) and Root Mean Square Error (RMSE) statistics were used to evaluate the performance of the machine learning (ML) algorithms. The RF and ME models demonstrated the highest performance levels. This research contributes to our understanding of landslide susceptibility in Badakhshan province and can aid in implementing effective landslide risk management strategies in the region.

Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)

The escalating number of occurrences of natural hazards such as landslides has raised a great interest among the geoscientists. Due to the extremely high number of point’s returns, airborne LiDAR permits the formation of more accurate DEM compared to other space borne and airborne remote sensing techniques. This study aims to assess the capability of LiDAR derived parameters in landslide susceptibility mapping. Due to frequent occurrence of landslides, Ulu Klang in Selangor state in Malaysia has been considered as application site. A high resolution of airborne LiDAR DEM was constructed to produce topographic attributes such as slope, curvature and aspect. These data were utilized to derive secondary deliverables of landslide parameters such as topographic wetness index (TWI), surface area ratio (SAR) and stream power index (SPI). A probabilistic based frequency ratio model was applied to establish the spatial relationship between the landslide locations and each landslide related factors. Subsequently, factor ratings were summed up to yield Landslide Susceptibility Index (LSI) and finally a landslide susceptibility map was prepared. To test the model performance, receiver operating characteristics (ROC) curve was carried out together with area under curve (AUC) analysis. The produced landslide susceptibility map demonstrated that high resolution airborne LiDAR data has huge potential in landslide susceptibility mapping.

Landslide susceptibility evaluation is critical for landslide prevention and risk management. Based on the slope unit, this study uses the information value method- random forest (IV-RF) model to evaluate the landslide susceptibility in the deep valley area. First, based on the historical landslide data, a landslide inventory was developed by using remote sensing technology (InSAR and optical remote sensing) and field investigation methods. Twelve factors were then selected as the input data for a landslide susceptibility model. Second, slope units with different scales were obtained by the r.slopeunits method and the information value method- random forest (IV-RF) model is used to evaluate the landslide susceptibility. Finally, the spatial distribution characteristics of landslide susceptibility grade under the optimal scale are analyzed. The results showed that under the slope unit obtained when c = 0.1 and a = 3 × 105 m2, the internal homogeneity/external heterogeneity of 8425 slope units extracted by the r.slopeunits method is the best, with an AUC of 0.905 and an F1 of 0.908. In this case, the accuracy of landslide susceptibility evaluation is the highest as well; it is shown that the finer slope units would not always lead to the higher accuracy of landslide susceptibility evaluation results; it is necessary to comprehensively consider the internal homogeneity and external heterogeneity of the slope units. Under the optimal slope unit scale, the number of landslides in the highly and extremely highly susceptible areas in the landslide susceptibility map accounted for 82.60% of the total number of landslides, which was consistent with the actual distribution of landslides; this study shows that the method, combining the slope unit and the information value method- random forest (IV-RF) model, for landslide susceptibility evaluation can obtain high accuracy.

China-Pakistan Economic Corridor (CPEC) is a framework of regional connectivity, which will not only benefit China and Pakistan but will have positive impact on Iran, Afghanistan, India, Central Asian Republic, and the region. The surrounding area in CPEC is prone to frequent disruption by geological hazards mainly landslides in northern Pakistan. Comprehensive landslide inventory and susceptibility assessment are rarely available to utilize for landslide mitigation strategies. This study aims to utilize the high-resolution satellite images to develop a comprehensive landslide inventory and subsequently develop landslide susceptibility maps using multiple techniques. The very high-resolution (VHR) satellite images are utilized to develop a landslide inventory using the visual image classification techniques, historic records and field observations. A total of 1632 landslides are mapped in the area. Four statistical models i.e., frequency ratio, artificial neural network, weights of evidence and logistic regression were used for landslide susceptibility modeling by comparing the landslide inventory with the topographic parameters, geological features, drainage and road network. The developed landslides susceptibility maps were verified using the area under curve (AUC) method. The prediction power of the model was assessed by the prediction rate curve. The success rate curves show 93%, 92.8%, 92.7% and 87.4% accuracy of susceptibility maps for frequency ratio, artificial neural network, weights of evidence and logistic regression, respectively. The developed landslide inventory and susceptibility maps can be used for land use planning and landslide mitigation strategies.

The research location is in the Purwanegara Subdistrict, Banjarnegara Regency, Province of Central Java. According to the Geological Agency of Indonesia, this location is a landslide-prone area, and many landslides occur yearly. Therefore, it needs a landslide susceptibility map is basic information for landslide mitigation in this area. The parameters used in this research are slope, lithology, distance from structural geology, distance from the river, land use, and rainfall. Using six parameters, the weight of evidence (WoE) method was used to develop a landslide susceptibility map. This research aims to make a zonation of landslide susceptibility in this area. The data were collected by field survey and visiting some related institutions. The landslide data used are 85 landslide events in the last three years. All data was then randomly divided into two groups: 70% as model data sets and 30% as validation data sets. The map of every parameter will be overlaid to create a landslide susceptibility zone. The Area Under Curve (AUC) method validated the final map. The result shows that landslide susceptibility zonation can be divided into four zones: very low susceptibility zone, low susceptibility zone, medium susceptibility zone, and high susceptibility zone. The very low susceptibility zone is mostly located in the north, the low susceptibility zone is mostly in the north and south, the moderate susceptibility zone is in the middle, and the high susceptibility zone is in the middle of the research’s location. The validation results of the success and prediction rates using the AUC method show an accuracy value of 0.86 and 0.82, respectively. According to the validation result, both can be classified as very good.