## 3 Results
### Variable EPA Method
Consequently, 44 learning objects of class \(H\) were recognized by both algorithms as non-hazardous for magnitude \(M\geq 6.0\). It means that the key differences in the classification belongs to a set of objects initially not attributed to learning sets (20 objects are classified identically by the algorithms and 10 are classified differently). It should be noted that the epicenters of earthquakes with \(M\geq 6.0\), used to form the learning set \(B_{0}\) (red circles in Figure 1) are located strictly within the vicinities of objects classified by both algorithms as high seismicity ones [16; 37]. It is noteworthy that the Barrier-3 algorithm is structured in such a way that learning objects in the final classification always belong to class \(B\). In turn, in recognition using dichotomy algorithms (particularly Cora-3 algorithm), learning objects are broadly speaking, not obliged to retain their attribution to the relevant class [4; 7]. The red star in Figure 2 shows the epicenter of the crustal earthquake, which occurred on 11 January 2021, with \(M=6.7\). This earthquake occurred after the completion of the independent recognitions described herein using the Barrier-3 and Cora-3 algorithms, thus representing the material for a pure examination for them. It can be seen from Figure 2 that the epicenter is located outside of the vicinities (with a radius of 25 km) of the intersections of lineaments recognized as high seismicity ones by both algorithms. At the same time, it is located 42 km away from the nearest recognition object attributed to class \(B\) by both algorithms. It was believed in the formation of learning material that the epicenter is confined to the intersection if it is located at a distance of no more than 50 km. Accordingly, the epicenter of the earthquake that occurred on 11 January 2021, is confined to the intersection of lineaments attributed to class \(B\) by both algorithms, yet is located outside of the 25 km of vicinities used to compute the values of its characteristics. The recognition results of the areas prone to earthquakes with \(M\geq 6.0\) in the Altai-Sayan-Baikal region, obtained using the Barrier-3 (one learning class) [16] and Cora-3 algorithms (two learning classes) [36; 37], are well aligned with each other. On the one hand, this evidences the reliability of both results since recognition was performed independently. On the other hand, in the interpretation of differences (15.5% of the total number of objects) in the outcomes, preference should be given to the classification using the Barrier-3 algorithm since it was performed with learning containing no protentional errors. The recognition result of the areas prone to strong earthquakes in the Caucasus, obtained using the Barrier-3 algorithm as the EPA recognition block, is shown in Figure 4 as ellipses with blue boundaries. Barrier-3 attributed 108 out of 237 intersections of lineaments to high seismicity class \(B\). Figure 5 shows the bar diagrams demonstrating the contribution of object characteristics to the recognition, by Barrier-3 algorithm, of the intersections of lineaments, in whose vicinities strong earthquakes can occur in the Caucasus. As it can be seen, the greatest contribution is made by the characteristics that are responsible for relief heights (Hmax and Hmin), the area of quaternary rocks (Q), the highest rank of lineament (HR), the number of lineaments in the vicinities (NLC), and the distances to the nearest lineaments of ranks I (R1) and II (R2). In the Caucasus, the intersections of lineaments recognized as hazardous ones for \(M\geq 6.0\) against the background of the entire set of recognition objects in their vicinities are characterized by high values of the maximum and minimum heights (Hmax \(\geq 2500\) m and Hmin \(\geq 600\) m) and a small area of quaternary rocks (Q \(\leq 30\%\)).