EXPLOITATION OF MCDA TO LEARN THE RADIAL BASE NEURAL NETWORK (RBFNN) AIM PHYSICAL AND SOCIAL VULNERABILITY ANALYSIS VERSUS THE EARTHQUAKE (CASE STUDY: SANANDAJ CITY, IRAN)

9 citations as recorded by crossref.

1. Efficiency of multiple hybrid techniques for the earthquake physical susceptibility mapping: the case of Abbottabad District, Pakistan A. Maqsoom et al. 10.1007/s12665-021-09964-1
2. An integrated approach based earthquake risk assessment of a seismically active and rapidly urbanizing area in Northern Pakistan A. Maqsoom et al. 10.1080/10106049.2022.2105404
3. Flood susceptibility mapping using an improved analytic network process with statistical models P. Yariyan et al. 10.1080/19475705.2020.1836036
4. A GIS-Based Assessment of Urban Tourism Potential with a Branding Approach Utilizing Hybrid Modeling M. Dadashpour Moghaddam et al. 10.1007/s41324-022-00439-4
5. Optimization of statistical and machine learning hybrid models for groundwater potential mapping P. Yariyan et al. 10.1080/10106049.2020.1870164
6. Earthquake Vulnerability Mapping Using Different Hybrid Models P. Yariyan et al. 10.3390/sym12030405
7. Earthquake risk assessment using an integrated Fuzzy Analytic Hierarchy Process with Artificial Neural Networks based on GIS: A case study of Sanandaj in Iran P. Yariyan et al. 10.1016/j.ijdrr.2020.101705
8. GIS-based spatial modeling of snow avalanches using four novel ensemble models P. Yariyan et al. 10.1016/j.scitotenv.2020.141008
9. Using Artificial Neural Networks to Assess Earthquake Vulnerability in Urban Blocks of Tehran R. Afsari et al. 10.3390/rs15051248