Rockfall is a type of landslide that can be defined as blocks detaching from a cliff face and heading rapidly downslope with different motion modes encompassing flying/falling, bouncing, rolling, and sliding [1
]. These blocks vary in size ranging from decimeters (boulder fragments) to meters (big rocks) [3
]. Such a phenomenon frequently occurs in mountainous and hilly areas with high and steep terrain such as Malaysia which can cause damage to human or material [4
]. Rockfall varies temporally and spatially, hence it is challenge to predict such events. Consequently, a proper rockfall hazard assessment is required to reduce or eliminate damages and save lives and properties through providing a comprehensive information of rockfall probability to the developers of urban areas.
The use of a digital terrain model (DTM) is a fundamental step in each rockfall hazard assessment. In recent decades, Light Detection and Ranging (LiDAR) techniques, both terrestrial laser scanner (TLS) and airborne laser scanner (ALS), have been increasingly employed in geo-hazard studies to create an accurate DTM [5
]. This is because of its ability to produce high-resolution data in both horizontal and vertical directions. However, LiDAR raw data include both ground and non-ground points. Therefore, data pre-processing, filtering and interpolation processes are required to remove the outliers (due to the presence of LiDAR system error and ray multipath) and the non-ground points (man-made and natural features) [2
]. DTM is the source of most significant rockfall conditioning factors such as altitude, slope, aspect, curvature, etc. [3
]. Nevertheless, other relevant conditioning factors such as hydrological, geological, anthropogenic, and environmental factors are also significant for rockfall probability mapping [3
]. Remote sensing (RS)-based data and Geographic Information System (GIS) tools, together with machine-learning approaches, have been popularly used in numerous studies. In recent studies, soft computing, heuristic, statistic, and deterministic models have been used to evaluate rockfall in terms of source identification [8
], kinematic modeling [11
], hazard assessment [1
], and risk assessment [16
]. The numerical modeling predicts either the blocks trajectories using Newtonian mechanics or the rockfall runout zone based on empirical measurements [2
]. The trajectories modeling represents the rockfall physics besides kinetic energy and bounce height of a moving block, which are highly significant in the design of mitigation processes [3
]. Several models have been developed for rockfall trajectory simulation. These models can be classified into three groups based on the properties of their simulation and terrain. First, the rockfall trajectories simulation can be performed utilizing either a probabilistic or deterministic approaches. Second, the rockfall kinematic can be simulated via a rigid body or a lumped mass method. Third, the topography of a slope can be represented by either a 3D terrain or a 2D profile [3
In comparison with analytic and expert opinion-based methods, machine-learning approaches are considered more effective for landslide spatial prediction, including rockfall [19
]. The essential conception of these approaches is the utilization of machine-learning classifiers for assessing rockfall probability through analyzing the spatial relationship between previous landslides incidents (i.e., inventory) and the conditioning factors [9
]. Various machine-learning classifiers have been developed and employed for generating landslide probability maps in different areas worldwide. This includes logistic regression (LR) [20
], random forest (RF) [22
], artificial neural network (ANN) [24
], support vector machine (SVM) [26
], and k-nearest neighbor (KNN) [28
Although numerous studies have been performed to assess rockfall hazard, the use of machine-learning classifiers is still very rare in this field, especially hybrid models. In addition, most of rockfall hazard studies have been conducted either for local scale (small area) or regional scale (large area) but performing a study in two different scales at the same time has not been done. Such a study produces an inclusive evaluation of rockfall in terms of sources, trajectories, their characteristics, leading up to hazard assessment. On the other hand, rockfall trajectories, velocity, frequency, kinetic energy, bouncing height, and impact locations were not considered in most of rockfall hazard assessment studies. Therefore, the current research optimizes a hybrid machine learning model based on various classifiers (LR, RF, ANN, SVM, KNN) for producing a rockfall probability map and thus identifying rockfall sources of the whole study area (regional scale), and employed a developed 3D rockfall kinematic model to assess rockfall trajectories and their characteristics for the two small areas within the study area (local scale). It also develops a spatial model based on fuzzy analytical hierarchy process (fuzzy-AHP) integrated into GIS to generate the final rockfall hazard maps. In addition, mitigation processes were suggested and assessed in these areas.
5.1. Rockfall Sources Identification
The multicollinearity of the conditioning factors was assessed utilizing the VIF process to analyze the correlations among these factors. According to the literature, a value of more than four is considered high collinearity. Therefore, such factors must be removed from the modeling [32
]. For the validation of the proposed hybrid models, an assessment was performed by comparing the base models with their stacking models. The stacking RF-KNN outperformed the single and hybrid models. Among the single models, RF accomplished the best result while LR achieved the worst accuracy. After optimizing the hybrid model based on different machine learning classifiers, the stacking RF-KNN model achieved the best performance based on the all accuracy metrics. Therefore, it was employed to produce the rockfall probability map of the whole study area using the conditioning factors and the inventory dataset. The stacking RF-KNN model predicted the rockfall probability ranging from 0 (no probable occurrence) to 1 (very-high probability of occurrence). The reclassified probability map shows that the Lang and Rapat areas are highly prone to rockfalls. In order to detect potential rockfall source areas, the reclassified slope raster was intersected with probability values resulted from the proposed hybrid model (stacking RF-KNN). In addition, the qualitative assessment of produced map shows that the rockfall inventories are typically situated in the regions of those detected as probable rockfall source areas indicating the robustness of the proposed hybrid model.
5.2. Rockfall Trajectories and Their Characteristics
The rockfall spatial distribution can be distinguished in both sites. For the Lang area, 6000 boulders were thrown with a spherical shape with a diameter of 0.75 m. The result shows that some rockfall trajectories extended to the urban area crossing the secondary road in the southern part of the study area, another rest just in front of a temple and beyond a situated fence (brown line in Figure 8
a), and others crossed the main road in the north region. Nevertheless, some trajectories could reach up to the river that is located next to the slope toe.
In the Rapat area, many rockfall trajectories were seen extending along the main road at the northern region and some of them crossed the secondary road at other parts. Nevertheless, except for the trajectories at the northern part, most of the trajectories tend to rest within the temple’s boundaries. Thus, the northern and the middle regions are exposed to rockfall hazard in comparison with other parts. In situ investigation and the inventory dataset have verified the probable areas that are predicted as a high potential of rockfall hazard. Many protection measures exist such as a barrier or fence, rockshed, net, fill, ditch, and berms. Nevertheless, utilizing each process relies on the applicability of a particular method and site condition [4
]. In this research, diverse protection measures were suggested according to the obtained rockfall frequency, energy, bouncing height, and the impact locations. In addition, we considered mitigation measures in selecting barrier locations and criteria.
During flying or free-falling movement modes, the falling boulders gain the highest velocity and energy in comparison with other motion modes. However, a rock with bigger diameter has higher energy at a given velocity. This is because the kinetic energy is a function of rock velocity and volume. In the Lang area, the middle and southern regions are exposed to the highest energy. In Rapat area the highest energy was detected in the middle to the south-eastern region and on the main road in the northern region.
The inventory database was used to validate the estimated rockfall frequency. The result shows that the modelling agrees with the recorded rockfall events, as the inventory dataset reflects the realistic spatial distribution of rockfall occurrences. In the Lang area, the frequency distribution is diverse along the analysis area. The part between the hill and the proposed barrier (black line in Figure 9
b) is estimated to encounter the highest frequency of rockfall, emphasizing the location correctness of the proposed barrier. In the Rapat site, the main road in the northern part is exposed to the highest frequency while in the south-eastern part, the highest frequency was observed close to the source areas.
Each rockfall trajectory was evaluated in terms of bounce height for each falling rock through calculating the highest bounce height in each raster cell. The highest bounce height was detected on a slope adjacent to the rockfall sources as falling boulders separate from a cliff with a high elevation. However, in the middle of the study area, some trajectories were observed to have high bouncing heights over the suggested obstacle (black line in Figure 9
c). This is because of the suggested obstacle being next to the slope foot. Such information is significant for supporting the mitigation processes design. For the Rapat area, the south-eastern part of the study area and the intersection of the main and secondary roads are exposed to the highest bouncing height.
The other significant factor in rockfall hazard assessment is the impact point which reflect the exact location of a falling rock. This is because knowing the impact location and the kinetic energy enables the determination of the destruction and resistance degree and the rockfall vulnerability (degree of loss). Consequently, rockfall risk can be evaluated based on rockfall hazards and vulnerability. Impact location is also highly significant for the planning of protection solutions. For example, an obstacle should be situated adjacent to these locations because a falling block has the lowest kinetic energy with bounce height of zero during the impact phase. Therefore, an obstacle could protect the probable areas effectively at these locations. In both study areas, the highest number of impact points was estimated adjacent to rockfall sources. For the Lang site, the urban area was estimated to be highly affected by bouncing falling boulders (Figure 9
d). However, the furthest side of the main road was noted to be completely safe from encountering impact locations. On the other hand, the highest impact locations number were also detected adjacent to the suggested obstacle position (black line), confirming the correctness of the proposed barrier location.
5.3. Rockfall Hazard Assessment
Rockfall hazard maps reflect the danger degree rather than single rockfall factors (frequency, bouncing height, kinetic energy, and impact location). For example, a given zone may have a moderate or high value of a hazard but a low value of a particular rockfall characteristics, and vice versa. The rockfall hazard maps were classified into five classes (very low, low, moderate, high, and very high), reflecting the degree of the hazard in a particular area.
In the Lang site, the existing temple in the northern part of the study site is close to the fuel station and the existing fence is prone to a hazard degree ranging from moderate to very high. The open land area in the middle of the site between the fuel station and the urban area is prone to a hazard degree ranging from very low to moderate. In the southern part of the site (close to a temple), the rockfall hazard ranges between moderate to high. For the two temples in the middle and southern parts of the site, the hazard degree ranges from high to very high. This is because of the closeness of these temples from the source areas.
In the Rapat area, the temples prone to rockfall hazards ranges from high to very-high. This is because these temples are located at the foot slope. The secondary road may encounter very low rockfall. This is because most of the falling rocks rest in the zone between the rockfall source areas and the existing fence.
5.4. Suggested Mitigation Approaches
Rockfall protection is a significant measure when defending exposed socio-economic related features or during the planning of a new urban development or industrial facilities and infrastructures in the rockfall vulnerable areas. The protection of rockfall involves hazard evaluation, identification of protection method, designing of a structural countermeasure, and finally the definition of a maintenance program [35
]. Such processes require an accurate quantification of rockfall block size distributions and susceptibility in the probable source regions, probable rockfall trajectories, intensity and distribution of impacts, statistical variability and magnitude of involved dynamic and kinematic quantities (e.g., velocity, kinetic energy, bouncing height).
In this research, various protection processes were suggested and evaluated based on the obtained rockfall factors including kinetic energy, impact location, bouncing height, and frequency. In addition, the sites’ conditions were also considered after applying a particular method. For an example, in the Lang area, a small portion in the middle of the site can face some rockfall trajectories due to its proximity to the barrier to the slope and the bouncing heights of some rockfall trajectories at that location. Although this area is open land, further attention can be paid to it and alternative mitigation process can be tested or a barrier can be constructed with a further distance from the source areas.
In the Rapat area, the existing fence could stop all rockfall trajectories distributed between the intersection area of the main road and the secondary one to the southeast part of the study area. Thus, it can protect the secondary road. However, the hazard still exists for the temple area especially between the hill and the fence. Therefore, rockfall sheds were proposed that can protect the area between the main entrance and the temple entrance because this is the most significant part where tourists visit. For the main road, due to it being near of the hill, a barrier cannot protect this part because not enough space is there to apply such a process. Therefore, a new location was proposed close to the source areas which can stop the small rocks and reduce the kinetic energy of the big rocks that can reduce the rockfall hazard degree.
In the Rapat area there is a fence existing within a distance from the rockfall sources with a height of 2 m. Therefore, we did not propose a barrier in this area. However, the existing fence was assessed and alternative mitigation processes were suggested. Figure 12
illustrates the assessment of the existing fence in terms of (a) kinetic energy, (b) velocity, (c) bouncing height, and (d) the reaching time factor.
Rockfall is one of the most hazardous phenomena in mountainous and hilly regions with high and steep terrain. Such incidents vary both spatially and temporally, thus it is challenging to assess these events accurately. Therefore, this research proposed a method for a comprehensive assessment of rockfall at two different scales (regional and local), in terms of source identification, trajectories, their characteristics, hazard, and mitigation processes. The main datasets used in rockfall hazard assessment are rockfall inventory, LiDAR, and GIS layers. The inventory dataset was prepared from different sources including field measurements. LiDAR techniques both aerial and terrestrial were used to capture the topography of the study areas. The ALS produced a point cloud with point density of 9 points per square meter with root-mean-square-error of 0.15 m vertically and 0.30 m horizontally. The TLS generated multi point clouds of millions of vector points with an accuracy of 3 cm. Filtering (to remove undesired or non-ground features) and interpolation processes were applied to generate the final DTM. A high-resolution and accurate DTM with a spatial resolution of (0.5 m) was produced using the LiDAR dataset. The LiDAR data, inventories, and the GIS database were finally registered and projected into the same projection system (UTM WGS84). Accordingly, various rockfall conditioning factors were derived including morphological and hydrological factors. Fifteen conditioning factors were prepared from different sources and the multicollinearity was assessed among these factors. The result shows that all of these factors are significant for rockfall probability assessment. A hybrid machine-learning model (stacking) were optimized based on diverse machine learning classifiers (LR, RF, KNN, SVM and ANN) using the inventory dataset and the prepared conditioning factors. The hyper-parameters of these classifiers were optimized using the grid search method. The stacking RF-KNN outperforms the other models with an accuracy of (89%), (86%), and (87%) based on training, validation, and cross-validation datasets, respectively. The rockfall probability map that produced through stacking RF-KNN was intersected with the reclassified slope raster based on a specific threshold to produce rockfall sources for the whole study area (regional scale).
The result shows that the Lang and Rapat areas are highly prone to rockfall hazard. Therefore, a developed 3D rockfall kinematic model was calibrated and employed to assess rockfall trajectories and their characteristics in terms of velocity, kinetic energy, frequency, bouncing height, and impact locations. Consequently, a developed spatial model combined with fuzzy-AHP was utilized to estimate rockfall hazard in the analysis areas. In addition, mitigation processes were suggested and assessed and the results prove that these processes can efficiently eliminate or reduce rockfall hazard. The proposed method can assist the decision-makers or the developer of urban areas through providing a detailed assessment of rockfall hazard in a particular area. The generated maps can be used to have a sustainable environment, avert more urbanization in hazardous regions, and to decrease the destruction and fatalities in case of a rockfall incident. Moreover, planners and governments can use the outcomes obtained through this research to distinguish safe areas for residents, support first responders in emergencies, and improve the strategies of urban planning. This information can reduce the necessity to conduct in situ investigation by agencies such as surveying departments. On the other hand, the obtained results can facilitate design in developing barriers and suggest different mitigation processes.