In recent years, population growth and urban development have contributed to an increase in natural disasters in both developed and developing countries (Huppert and Sparks, 2006), but are generally more serious in developing countries (Alcántara-Ayala, 2002). Landslides are among the most common damaging geohazards, especially in the mountainous regions. Landslide is a general term for a variety of mass movements in soil or rock moving downslope by gravity (Malamud et al., 2004). Over the last century (1903-2004), landslides alone accounted for 17% of the world’s natural disasters, with the highest annual damage in Europe estimated at US$17 million (Koehorst et al., 2005). Landslides have resulted in hundreds of billions of dollars in damages to the built environment, thousands of fatalities, and numerous environmental impacts and are an important driver of landscape change (Aleotti and Chowdhury, 1999; Schuster and Highland, 2001; Geertsema et al., 2009; Fan et al., 2019; Kadirhodjaev et al., 2020). In developing countries, more than 0.5% of the Gross Domestic Product (GDP) is lost every year due to landslides (Chen et al., 2015).

The topic of landslide susceptibility mapping (LSM) has become increasingly popular over the last decade and is being continuously fine-tuned to mitigate landslide hazards, inform land use planning, and improve the prediction accuracy of upcoming landslides. The core idea of LSM has been to explore the association between historical landslides and different causing factors for the prediction of the likelihood of upcoming landslides. To analyze the associations between historical landslides and causing factors, researchers have suggested and used many methods that range from simple and straightforward expert-based and statistical methods to advanced and complex methods derived from machine learning. The expert knowledge methods such as analytical hierarchy process (AHP) (Althuwaynee et al., 2016) and spatial multicriteria evaluation (SMCE) (Meena et al., 2019) and bivariate and multivariate statistics such as frequency ratio (FR) (Chen et al., 2015), weights of evidence (WoE) (Razavizadeh et al., 2017), weighted linear combinations (WLC) (Hung et al., 2016), statistical index (SI) (Razavizadeh et al., 2017), certainty factor (CF) (Wang et al., 2019), index of entropy (IOE) (Chen et al., 2015), and logistic regression (LR) (Sun et al., 2021b) are the first generation methods used for LSM mapping worldwide, with clear processes and are easy to understand and interpret outcomes.

The next generation of the methods used for LSM has originated from machine learning that involves hundreds of algorithms such as artificial neural network (ANN) (Lucchese et al., 2021), adaptive neurofuzzy inference system (ANFIS) (Jaafari et al., 2017), random forest (RF) (Park and Kim, 2019), support vector machine (SVM) (Yao et al., 2008), decision tree (DT) (Dou et al., 2019a), Naïve Bayes (NB) (Nguyen and Kim, 2021), Bayesian logistic regression (BLR) (Abedini et al., 2019), best first decision tree (BFT) (Chen et al., 2018), and deep learning neural network (Ghasemian et al., 2022). With the improvement of artificial intelligence, machine learning becomes the most applied approach for LSM currently.

The other stream of research on machine learning modeling of LSM has combined different methods/algorithms to achieve more accurate prediction results. For example, Pham et al. (2017) reported that the rotation forest (RoF) technique improved the predictive ability of the Naïve Bayes tree landslide prediction. Nguyen et al. (2017) showed that the landslide predictive ability of the instance based learning classifier can be improved by the RoF technique. He et al. (2019) combined the Creedal Decision Tree with the RoF technique and achieved improved accuracy for landslide prediction. In a recent study, Fang et al. (2021) demonstrated that the performance of the decision tree models could be significantly improved when they were integrated with the RoF technique. The key advantage of the RoF technique as a meta classifier is that it can balance accuracy and diversity and decrease bias and overfitting of the modeling process.

Reliability and accuracy of future probabilities are the most important characteristics of a landslide susceptibility map. While machine learning is now widely used in LSM, there is no best method for accurately predicting landslides, especially not for the regions of varying levels of geoenvironmental complexity. However, the experience, to date, suggests comparing several different methods and selecting the optimal one to generate an accurate landslide susceptibility map for a given region. Furthermore, the evaluation of the usefulness of different conditioning factors via feature screening techniques and the optimization of different methods in terms of parameters are other important subjects in the field of landslide modeling (Sun et al., 2021a; Zhou X. et al., 2021).

Iran’s vast mountainous areas have been shaped and modified by ongoing tectonic forces, producing faults, fractures, and sensitive lithology, priming the country for landslides (Shafizadeh-Moghadam et al., 2019). Increased developmental activities and industrial and agricultural and human encroachment on the natural environment due to the extensive land use change in forested areas in recent decades have increased the vulnerability to landslides. Susceptibility mapping and understanding landslide mechanisms, in order to reduce or control landslide damage, are necessary.

In this study, we combined a metaclassifier algorithm with a standalone algorithm as a base classifier to increase the predictive power of the base classifier by reinforcing the parameters used in the model during the calibration phase. The main contribution of our study is to explore how the RoF classifier and a random forest (RF) decision tree classifier generate a hybrid predictive model, called RoFRF, that provides an opportunity to pilot hybrid modeling of LSM and insights into feature screening and selection and parameter optimization. We developed the hybrid RoFRF model using the datasets belonging to Asadi et al. (2022) and Ghasemian et al. (2022) from the Kamyaran area in the Kurdistan Province, Iran, but with a different set of algorithms and results.

The study area is in the southwest of the Kurdistan Province covering an area of about 150 km² (Figure 1). The minimum and maximum elevations of the study area are 850 and 2,328 m, respectively, with a height difference of 1,478 m (Figure 1). The average annual rainfall for the period from 2001 to 2019 ranges from 438 to 560 mm and the average annual temperature is 14.15°C. Based on the De Martonne climatic classification index, the climate of the study area is semi-arid climate (Asadi et al., 2022). Bedrock geology belongs to the structural zone of Sanandaj-Sirjan and the high Zagros zone, typified by basalts and shales with the intercalations of volcanic rocks. The six main land cover classes include dry farming, semi-dense forest, low-dense forest, semi-dense pasture, dense pasture, and woodland. The predominant land covers in the study area are semi-dense forests and dry farming (Ghasemian et al., 2022).

The study area involved by active faults from the Zagros Main Recent Fault and its formations such as marl and shale and also due to the topographical conditions and geomorphological process (steep slopes and soil erosion) and also anthropogenic factor or improper human interferences (e.g., road construction on Kashtar to Yozidar route and the removal of slope bases) resulted in some landslides occurrence, considering the study area as one of the most susceptible regions of the Kurdistan Province and the country (Asadi et al., 2022). Landslides of the study area are typically shallow with the rupture surfaces less than 2–3 m depth. Figure 2 shows photos of a number of the landslides that occurred in the study area.

The methodology of this research is shown in Figure 3. We selected 25 conditioning factors for the terrain hosting landslides derived from the topographical, geological and land cover maps, meteorological data, digital elevation model (DEM), documentary sources, field surveys, and Google Earth Imagery. We classified our landslides into two groups; training landslides (80%) and validation landslides (20%) (Xie et al., 2021a; Xie et al., 2021b). We then developed the hybrid RoFRF model and compared its performance to the four benchmark models including RF, ANN, BFT, and logistic model tree (LMT) using area under ROC and other statistical measures. The main steps of the methodology are described in the following subsections.

The main objective of our study was to model the spatial distribution of landslide susceptibility and to produce a susceptibility map with high prediction reliability. Therefore, we focused on evaluating the performance of different machine learning methods as the crucial step of a landslide modeling project (Brenning, 2005; Reichenbach et al., 2018). While numerous methods have been suggested and used for landslide modeling over the past decades, machine learning methods have been preferred by many researchers (Merghadi et al., 2020). In recent years, the efficiency of ensemble learning techniques in improving the performance of the machine learning methods has been acknowledged by some researchers (Pham et al., 2020b; Nhu et al., 2020). To test the performance of single models against an ensemble model, we first measured the significance of the conditioning factors using the Chi-square technique with 10-fold cross-validation and identified the distance to roads and road density as the most significant factors related to landslide occurrences in the study area. Similar results have also been reported from other regions, where transport infrastructure cross-steep terrain (Jaafari et al., 2017; Schlögl and Matulla, 2018). Old road networks, which were once planned for low traffic and axle loads, are at extremely high risk of landslides. Hence, maintenance and landslide mitigation measures for these roads should be considered (Schlögl et al., 2019).

We assessed the models’ results via a validation process to compare the ability of four models developed to spatially predict landslides. 10 performance measures indicated that the RF model had a better goodness-of-fit (using the training dataset) and prediction ability (using the validation dataset) than that of the other three single models (i.e., ANN, LMT, and BFT). Many other studies have demonstrated the superiority of the RF model to other machine learning methods, such as best-first decision tree and Naïve Bayes tree (Chen et al., 2018), artificial neural network, and logistic regression (Smith et al., 2021). RF is a powerful nonlinear machine learning method intended for solving classification problems that can overcome the multicollinearity and nonlinear dependencies among the variables (Boulesteix et al., 2012). Being a nonparametric method, RF can be regarded as the most flexible machine learning method (James et al., 2021) with the ability to handle multiclass and skewed datasets (Guyennon et al., 2021). Given this superiority, we selected the RF model as the base model for combining it with the RoF technique to develop a hybrid predictive model, i.e., RoFRF. The new hybrid RoFRF model significantly improved the prediction performance of the base RF model. This is reasonable because the models developed based on the ensemble learning techniques can reduce both the variance and bias of the modeling process and avoid overfitting to gain the highest prediction performance (Nhu et al., 2020; Tran et al., 2020). The key point for the efficiency of the RoF technique is to increase the diversity and individual accuracy of the ensemble classifier simultaneously. Diversity is promoted via the principal component analysis (PCA) to perform feature extraction for the base classifier, whereas accuracy is achieved by using all the principal components and also the whole dataset to train the base classifier (Park et al., 2019). Similar studies have also shown that the RoF technique can improve the training performance (i.e., goodness-of-fit) and validation performance (i.e., predictive ability) of the base classifiers for landslide prediction. In sum, the RoF technique with a fast performance has a decent generalization capability and low implementation complexity, that make it a favored choice for developing powerful ensemble models for landslide prediction.

Overall, our study demonstrated that for a certain study area it is reasonable to select the most influential controlling factors via the feature screening methods and to identify the most accurate method via parameter optimization and comparing multiple models. A comparative approach allows for investigating the capability of multiple models for producing the most accurate and reliable susceptibility maps. This approach is an improvement to the traditional approach that often selects a single model and may ignore the other potentially better models for prediction. Therefore, the hybrid modeling provides a framework that accurately analyzes the historical landslides and conditional factors and improves the reliability of the prediction of future landslides.

The aim of this study was to perform a hybrid model of Rotation Forest - Random Forest (RoFRF) and its comparison with the Artificial Neural Network (ANN), Logistic Model Tree (LMT), Best First Tree (BFT), and Random Forest (RF) models to map landslide susceptibility in the part of Kamyaran area in Kurdistan Province, Iran. To achieve this goal based on different sources, 25 landslides affecting (or controlling) factors: elevation, slope angle, aspect, curvature, profile curvature, plan curvature, solar radiation, VRM, VD, SPI TWI, TRI, TPI, LS, NDVI, rainfall, distance to fault, distance to road, distance to river, fault density, road density, river density, lithology, land use, and soil were selected and applied as inputs to the models. Then, the relative importance of each factor was examined based on the Average Merit (AM) score. Among all the factors, 16 factors were importantly selected and used for the modeling procedure. In the next step, after drawing the landslide inventory map, a set of training and validation datasets were divided respectively, for modeling and evaluation processes. The hybrid proposed method can derive the benefits of basic classifiers using different group learning strategies. The present study demonstrated an efficient way to combine different types of landslide susceptibility methods, hybrid learning, and deep learning to obtain a more accurate map. Based on this, the most important findings of our study are summarized as follows:

1) Identifying the most influential controlling factors in the occurrence of shallow landslides and the preparation of susceptibility maps are the basic strategies to control this phenomenon and select the most appropriate and practical options. Although according to the AM score, 16 factors affected the occurrence of shallow landslides, and the most important factor was the distance to roads, followed by the road density factor. Our results demonstrated that more careful road construction, maintenance, and route planning needs to be considered to reduce future landslide occurrence.