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http://hdl.handle.net/123456789/794
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| Title: | A comparison of ANFIS and ANN for the prediction of Peak Ground Acceleration in Indian Himalayan Region. |
| Authors: | Mittal, Abha
Sharma, Shaifaly
Kanungo, D P |
| Keywords: | Peak ground acceleration (PGA)
Adaptive neuro-fuzzy inference
System (ANFIS)
ANN
Root-mean-square error
Modeling
2011 |
| Issue Date: | 5-Mar-2012 |
| Abstract: | Peak ground acceleration (PGA) plays an important role in assessing
effects of earthquakes on the built environment, persons, and the natural environment.
It is a basic parameter of seismic wave motion based on which earthquake
resistant building design and construction are made. The level of damage is,
among other factors, directly proportional to the severity of the ground acceleration,
and it is important information for disaster-risk prevention and mitigation
programs. In this study, a hybrid intelligent system called ANFIS (the adaptive
neuro fuzzy inference system) is proposed for predicting Peak Ground Acceleration
(PGA). Artificial neural network and Fuzzy logic provide attractive ways to
capture nonlinearities present in a complex system. Neuro-Fuzzy modelling,
which is a newly emerging versatile area, is a judicious integration of merits of
above mentioned two approaches. In ANFIS, both the learning capabilities of a
neural network and reasoning capabilities of fuzzy logic are combined in order to
give enhanced prediction capabilities, as compared to using a single methodology
alone. The input variables in the developed ANFIS model are the earthquake
magnitude, epi-central distance, focal depth, and site conditions, and the output is
the PGA values. Results of ANFIS model are compared with earlier results based
on artificial neural network (ANN) model. It has been observed that ANN model
performs better for PGA prediction in comparison to ANFIS model. |
| Description: | In: K. Deep et al. (Eds.): Proc. of the International Conf. on SocPros 2011, AISC 131, pp. 457-468. |
| URI: | http://hdl.handle.net/123456789/794 |
| Appears in Collections: | Published Articles
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