ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W4, 189-195, 2017
https://doi.org/10.5194/isprs-annals-IV-4-W4-189-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
 
13 Nov 2017
VULNERABILITY INDEX ASSESSMENT USING NEURAL NETWORKS (VIANN): A CASE STUDY OF NICOSIA, CYPRUS
H. Dindar1,2, K. Dimililer3, Ö. C. Özdağ4, C. Atalar2,5, M. Akgün6, and A. Özyankı2,5 1Graduate School of Natural and Applied Sciences, Dokuz Eylül University, İzmir, Turkey
2NEU Earthquake and Soil Research and Evaluation Center, Near East University, Nicosia, North Cyprus, Mersin 10, Turkey
3Dept. of Electrical and Electronic Engineering, Near East University, Nicosia, North Cyprus, Mersin 10, Turkey
4Aegean Implementation and Research Center, Dokuz Eylül University, İzmir, Turkey
5Dept. of Civil Engineering, Near East University, Nicosia, North Cyprus, Mersin 10, Turkey
6Dept. of Geophysical Engineering, Dokuz Eylül University, İzmir, Turkey
Keywords: Vulnerability Index, Microtremor, Back Propagation Neural Networks, Sensitivity Zones, Nakamura Method Abstract. Many scholars have used microtremor applications to evaluate the vulnerability index. In order to reach fast and reliable results, microtremor measurement is preferred as it is a cost-effective method. In this paper, the vulnerability index will be reviewed by utilization of microtremor measurement results in Nicosia city. 100 measurement stations have been used to collect microtremor data and the data were analysed by using Nakamura’s method. The value of vulnerability index (Kg) has been evaluated by using the fundamental frequency and amplification factor. The results obtained by the artificial neural network (ANN) will be compared with microtremor measurements. Vulnerability Index Assessment using Neural Networks (VIANN) is a backpropagation neural network, which uses the original input microtremor Horizontal Vertical Spectrum Ratio (HVSR) spectrum set. A 3-layer back propagation neural network which contains 4096 input, 28 hidden and 3 output neurons are used in this suggested system. The output neurons are classified according to acceleration sensitivity zone, velocity zones, or displacement zones. The sites are classified by their vulnerability index values using binary coding: [1 0 0] for the acceleration sensitive zone, [0 1 0] for the velocity sensitive zone, and [0 0 1] for the displacement sensitive zone.
Conference paper (PDF, 1237 KB)


Citation: Dindar, H., Dimililer, K., Özdağ, Ö. C., Atalar, C., Akgün, M., and Özyankı, A.: VULNERABILITY INDEX ASSESSMENT USING NEURAL NETWORKS (VIANN): A CASE STUDY OF NICOSIA, CYPRUS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-4/W4, 189-195, https://doi.org/10.5194/isprs-annals-IV-4-W4-189-2017, 2017.

BibTeX EndNote Reference Manager XML