A Comprehensive Review On Landslide Susceptibility Zonation Techniques

FR is a statistical method used for landslide susceptibility mapping (Thakur, Kumar 2013, Sharma, Kumar 2015, Nath, Roy 2018, Kumar et al. 2019, Fatah et al. 2023, Thambidurai et al. 2023, Yadav et al. 2023). This method is based on the statistical analysis of the relationship between the landslide locations and the factors that may influence the landslide occurrence. The FR method has been widely used for landslide susceptibility mapping in different regions of the world, including the Himalayas. The FR method is a ratio of the number of landslides to the number of non-landslides in a given area for a particular parameter. It provides a value for the susceptibility of the study area for each parameter. The formula for the FR method is as follows: 1FR= (Number of landslides in a specific zone / Total number of landslides) (Number of non-landslide points in the same zone / Total number of non-landslide points)$${\rm{FR}} = {{{\rm{(Number of landslides in a specificzone / Total number of landslides)}}} \over {{\rm{(Number of non - landslide points in thesame zone / Total number ofnon - landslide points)}}}}$$

The output of the FR method is a susceptibility map, where the areas with higher FR values indicate a higher probability of landslide occurrence. The FR method has advantages such as its simplicity and ease of use, which make it a suitable method for rapid mapping in areas with limited data. However, the FR method has some limitations. It assumes that each parameter is independent of every other, which may not be the case in reality. In addition, it does not take into account the spatial correlation of the parameters, which may lead to the overestimation or under-estimation of the landslide susceptibility in some areas. Despite its limitations, the FR method is a useful tool for preliminary landslide susceptibility mapping in areas with limited data. It can also be used in conjunction with other methods to improve the accuracy of landslide susceptibility mapping.

Advantages: –

Easy to use and understand: The FR method is a simple and intuitive method that can be easily used by non-experts. It only requires the knowledge of the occurrence of landslides and the related causative factors,

Disadvantages: –

Over-reliance on past occurrences: The FR method heavily relies on past occurrences of landslides to predict future susceptibility, which may not always be accurate due to changing environmental and climatic conditions,

The IV method is a statistical method used in landslide susceptibility mapping (Kumar et al. 2019, Yadav et al. 2023). It is a measure of the strength of the relationship between a binary response variable (landslide occurrence or non-occurrence) and an explanatory variable (e.g., slope, aspect, lithology, etc.). The IV method can be used to rank the importance of different explanatory variables and to identify the most influential factors in landslide occurrence.

The formula for IV is as follows: 2IV =ln (odds ratio) )×(Pevent −Pnon-event ),$${\rm{IV}} = \ln ({\rm{odds ratio}}) \times \left( {{{\rm{P}}_{{\rm{event}}}} - {{\rm{P}}_{{\rm{non - event}}}}} \right),$$ where: –

ln indicates natural logarithm,

The IV method is commonly used in LR models to identify the most important predictors of landslide occurrence. The IV values range from 0 to infinity, with higher values indicating stronger relationships between the explanatory variable and landslide occurrence. An IV value of 0 indicates that the explanatory variable has no predictive power for landslide occurrence. The IV method has been widely used in landslide susceptibility mapping studies, including in the Indian Himalayas (Sharma et al. 2019, 2021). It has been shown to be an effective method for identifying the most important landslide-causative factors and for producing accurate landslide susceptibility maps.

Advantages: –

The IV method is simple and easy to use. It does not require extensive knowledge of statistical and mathematical techniques,

Disadvantages: –

The method assumes of independence of variables, which may not always be true,

The WoE method is a statistical approach used for landslide susceptibility mapping (Neupane et al. 2023), which evaluates the relationships between the landslide occurrences and the potential landslide-causative factors by quantifying the WoE supporting the presence or absence of the factors (Carrara et al. 1992). The WoE method is based on the Bayesian probability theory and provides a simple and objective means of evaluating the likelihood of an event occurring. The method quantifies the evidence that a particular factor is associated with the landslide occurrence and produces a WoE factor map showing the spatial distribution of the factors contributing to landslide occurrence (Ohlmacher, Davis 2003).

The formula for the WoE method is: 3WoE=ln (number of landslide occurrences / number of non-landslide occurrences) (number of landslide occurrences in the study area / number of non-land-slide occurrences in the study area)$${\rm{WOE}} = {{\ln {\rm{(number of landslide occurrences /number of non - landslide occurrences)}}} \over {{\rm{(number of landslide occurrences inthe study area / number of non - land - slide occurrences in the study area)}}}}$$

The WoE method has several advantages, including its simplicity, objectivity and ability to handle missing data. The method is also able to evaluate the relative importance of each factor in the susceptibility analysis. However, the method requires a large number of samples to provide reliable results and is sensitive to the choice of the reference data used in the analysis (Lee et al. 2019). Overall, the WoE method is a useful tool for landslide susceptibility mapping in areas with limited data and can provide valuable information for land-use planning and decision-making.

Advantages: –

WoE method is easy to implement and interpret, making it suitable for use by non-experts, – It can handle both categorical and continuous variables, providing more flexibility in model development,

The results of the method may be sensitive to the choice of weight and cut off values used in the analysis.

LR analysis is a statistical technique used to model the probability of an event occurring (Yadav et al. 2023). In the context of landslide susceptibility, LR analysis can be used to identify the factors that are most strongly associated with the occurrence of landslides. According to Chae et al. (2012), LR analysis is a widely used method for landslide susceptibility assessment. The technique allows for the identification and quantification of the factors that contribute to landslide occurrence, by estimating the probability of landslide occurrence based on the values of the independent variables. The model coefficients provide information on the strength and direction of the association between each independent variable and the probability of landslide occurrence.

To use LR for landslide susceptibility analysis, the landslide occurrence data are typically used as the dependent variable, with the landslide or non-landslide status of each location as the binary outcome. The independent variables, which are the factors that are thought to influence landslide occurrence, can include a variety of physical and environmental variables such as slope, geology, land use, precipitation and vegetation cover. Once the model is developed, it can be used to produce a landslide susceptibility map, which shows the probability of landslide occurrence across the study area. Areas with a higher probability of landslide occurrence are often considered to be more susceptible to landslides.

Advantages: –

LR can handle both continuous and categorical independent variables,

Disadvantages: –

LR assumes that the relationship between the independent variables and the log odds of landslide occurrence is linear, which may not always be the case,

ANN is a computational method inspired by the structure and function of biological neural networks. It consists of interconnected nodes or neurons that process information in a parallel, distributed manner. ANN has been widely used in various fields, including landslide susceptibility mapping. According to Ayalew et al. (2004), ANN is a powerful tool for modelling complex nonlinear relationships between landslide occurrence and various contributing factors. In the context of landslide susceptibility mapping, ANN can be used to classify the input data into landslide or non-landslide categories based on the relationship between the input variables and the output class.

ANN models are typically trained using a supervised learning approach, where the model is trained on a set of input–output pairs to learn the relationship between the input variables and the output class. The model can then be used to predict landslide susceptibility for new locations based on their input variables. The advantage of ANN is that it can handle complex nonlinear relationships and is less sensitive to outliers than other statistical techniques. However, ANN requires a large amount of training data and can be computationally intensive.

Advantages: –

ANNs can handle complex nonlinear relationships between landslide occurrences and their contributing factors,

Disadvantages: –

ANNs require a large amount of training data, which may not be available in some cases,

SVM is a machine learning method used for classification and regression analysis. SVM is based on the concept of finding the hyperplane that maximises the margin between the two classes. SVM has been widely used in landslide susceptibility mapping due to its ability to handle nonlinear relationships and its good performance on small and high-dimensional datasets. According to Gokceoglu et al. (2005), SVM is a powerful tool for landslide susceptibility mapping, as it can effectively classify the input data into landslide or non-landslide classes based on the relationship between the input variables and the output class. In the context of landslide susceptibility mapping, SVM can be used to identify the most important factors contributing to landslide occurrence and to develop accurate landslide susceptibility maps.

SVM works by transforming the input data into a high-dimensional feature space, where the classes can be separated by a hyperplane. The optimal hyperplane is selected based on the margin between the two classes, with the goal of maximising the margin while minimising the classification error. SVM can handle both linear and nonlinear relationships by using different types of kernel functions. The advantage of SVM is that it can handle high-dimensional data and can provide a clear separation between the two classes. However, SVM requires careful selection of model parameters, such as the kernel function and the regularisation parameter, and can be computationally intensive.

Advantages: –

SVM can handle high-dimensional data and can provide a clear separation between the two classes,

Disadvantages: –

SVM requires careful selection of model parameters, such as the kernel function and the regularisation parameter, which can be time-consuming and require expert knowledge,

Random forest is a machine learning algorithm used for classification and regression analysis. In the context of landslide susceptibility mapping, random forest has been widely used due to its ability to handle high-dimensional data and its good performance on large and complex datasets. Random forest works by constructing a multitude of decision trees based on random subsets of the input data and input variables. Each tree provides a classification result, and the final prediction is made based on the majority vote of all trees. Random forest can handle nonlinear relationships between landslide occurrences and their contributing factors and can identify the most important factors contributing to landslide susceptibility.

According to Kavzoglu and Sahin (2009), random forest is a powerful tool for landslide susceptibility mapping, as it can provide accurate and robust results even in the presence of noisy and missing data. Random forest can also provide information on the relative importance of each input variable, which can help to identify the most critical factors for landslide susceptibility. One advantage of random forest is that it can handle large and complex datasets with a large number of input variables. However, random forest can be computationally intensive and may require careful selection of model parameters, such as the number of trees and the maximum depth of each tree.

Advantages: –

Random forest is an ensemble method that combines multiple decision trees, which can lead to better prediction accuracy and more robust results compared to single decision trees,

Disadvantages: –

Random forest can be computationally intensive and may require a large amount of memory, especially for large datasets with a large number of input variables,

Ensemble approaches are machine learning techniques that combine multiple models to improve the accuracy and robustness of landslide susceptibility mapping (Fatah et al. 2023, Matougui et al. 2023). The most common ensemble methods used in landslide susceptibility mapping are bagging, boosting and stacking.

Bagging (Bootstrap Aggregating) involves creating multiple models using random subsets of the input data and input variables, and then aggregating the results to produce a final prediction. Bagging can reduce the variance and improve the generalisation ability of the models.

Boosting involves iteratively training multiple weak models, with each subsequent model focussing on the errors of the previous models. Boosting can improve the accuracy and robustness of the models, and is especially useful when dealing with imbalanced data.

Stacking involves combining multiple models with different architectures or algorithms, and using a meta-model to learn the optimal way to combine the results of the base models. Stacking can improve the accuracy and robustness of the models by combining the strengths of different methods.

Ensemble approaches have been shown to improve the accuracy and robustness of landslide susceptibility mapping compared to single models. For example, Kavzoglu, Sahin (2011) used a bagging ensemble of decision trees to map landslide susceptibility in the Yenice region of Turkey, and found that the ensemble approach outperformed single decision trees and other machine learning methods. Another study by Pham et al. (2018) used a stacking ensemble of LR, SVMs and random forest to map landslide susceptibility in the Song Pha watershed of Vietnam, and found that the stacking approach improved the prediction accuracy compared to single models.

Advantages: –

Improved prediction accuracy: Ensemble methods combine multiple models, which can lead to improved prediction accuracy compared to single models,

Disadvantages: –

Computationally intensive: Ensemble methods can be computationally intensive, as they require training multiple models and combining their results,