| | References | Study area | Utilized parameters | Utilized techniques |
| | Jena et al. 2021 [28] | Northeast, India | Distance from railway, railway density, distance from landuse, landuse density, distance from buildings, and building density | Analytical hierarchy process (AHP) and convolutional neural network (CNN) | | Yariyan et al. 2020 [4] | Sanandaj City, Iran | Building materials, landuse, distance from hospital, distance from fire station, number of floors, distance from street, altitude, lithology, distance from the fault, slope, distance from the stream, and population density | Fuzzy-multiple criteria analysis (fuzzy-MCDA) and logistic regression | | Lee et al. 2019 [31] | Tehran, Iran | Peak ground acceleration (PGA), slope, construction (material, quality), population, employment status, open spaces, road network, fire stations, hospitals, gas pipes, and gasoline stations | Radial basis function (RBF) and teaching–learning-based optimization (TLBO) | | Jena et al. 2019 [32] | Banda Aceh, Indonesia | Slope, curvature, elevation, aspect, epicenter density, epicenter distance, depth density, magnitude distribution, PGA density, fault, building density, office, population, and transport nodes | Artificial neural network (ANN) and AHP | | Liu et al. 2019 [33] | Urumqi, China | Type of structure, period of construction, number of floors, land use and land cover, and roof type | SVM and association rule learning (ARL) | | Hopkins and Turan et al. 2018 [34] | Turkey | Topography, source to site distance, soil classification, liquefaction potential, and fault mechanism | AHP, technique for order preference by similarity to ideal solution (TOPSIS) | | Ahmad et al. 2017 [35] | Syria | PGA, earthquake epicentres, active faults, digital elevation model, and slope | Earthquake potential index (EPI) |
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