Prediction of felt intensity from ground motion parameters using artificial neural network method

Download

index.pdf

Date

2019

Author

Öztürk , Seda

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

198
views

86
downloads

Earthquakes are natural phenomena that cause ground shaking and deformations due to the nature of the Earth's surface, which is composed of tectonic plates. The sudden release of energy on these tectonic plates results in earthquakes. One of the ways to measure ground shakings is the macroseismic (or felt) intensity. There are various studies on the correlation between felt intensity and ground motion parameters. Most of them involve a linear regression method to find an empirical formula for this relation. However, assuming a linear correlation may not the best approach since the independent variables affecting intensity values show highly non-linear behaviour. Therefore, a more flexible model capturing the complexities of these independent variables should be constructed. In this thesis, initially, principal component analysis (PCA) is applied to identify main independent variables that affect felt intensity. Based on the results of PCA and expert knowledge, various artificial neural network (ANN) models are built. Feedforward backpropagation method is used with different combinations of input variables to study the best predictions of MMI. Most of the ANN models resulted in better MMI estimations than those provided in the literature.

Subject Keywords

Principal components analysis., Keywords :Modified Mercalli Intensity (MMI), Principal Component Analysis, Artificial Neural Network Method, Seismic Ground Motion

URI

http://etd.lib.metu.edu.tr/upload/12625156/index.pdf
https://hdl.handle.net/11511/46151

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Numerical Modeling of Long Waves from Atypical Sources: Atmospheric Disturbances and Volcanic Origin Doğan Bingöl, Gözde Güney; Yalçıner, Ahmet Cevdet; Department of Civil Engineering (2022-6-30) Earthquakes are the main cause mechanisms of tsunamis and large tsunamigenic earthquakes, especially in the subduction zones, occur on relatively much shorter timescales, but destructive tsunamis are also produced by volcanic eruptions, which have been threatening the coastal communities throughout history. Furthermore, while earthquake-generated tsunamis have been studied immensely, there is less focus on research related to tsunamis induced by atmospheric disturbances (meteotsunamis). Consequently, this s...
Evaluation of the site effects of the Ankara basin, Turkey Kockar, M. K.; Akgün, Haluk (2012-08-01) Recent major earthquakes have explicitly demonstrated that near-surface local site conditions that can generate significant amplifications and spatial variations of earthquake ground motion play a major role in the level of ground shaking and in gathering information on soft soil response. It is therefore highly desirable to develop methods to identify and characterize regions that are prone to this type of site amplification. To determine the subsurface sediment characteristics over a wide area, measuremen...
The Prediction of Nonlinear Polar Motion Based on Artificial Neural Network (ANN) and Fuzzy Inference System (FIS) Kuçak, Ramazan Alper ; Uluğ, Raşit; Akyılmaz, Orhan (Springer International Publishing, 2018-01-01) The Earth rotation movement characterizes the situation of the whole Earth movement, as well as the interaction between the Earth’s various layers such as the Earth’s core, mantle, crust, and atmosphere. Prediction of the Earth rotation parameters (ERPs) is important for near real-time applications including navigation, precise positioning, remote sensing and landslide monitoring, etc. In such studies, the analysis of time series is also important for highly accurate and reliable predictions. Therefore, pre...
Estimation of earthquake insurance Premium rates Turkish catastrophe insurance pool case TEMOÇİN, BÜŞRA ZEYNEP; Kestel, Sevtap Ayşe (2016-06-01) Earthquakes are the natural catastrophes which have the highest unpredictability; destructive earthquakes appear less frequently in time and space. However, the financial impact of such earthquakes on human lives and economies is disastrous. The prediction on the occurrence of an earthquake in time, magnitude and location is expressed in terms of their joint probabilities. The estimation on the economic losses mainly depend on the properties of the structure. The variability in these variables makes it diff...
Spatial sensitivity of seismic hazard results to background seismic activity models Yilmaz, N.; Yücemen, Mehmet Semih (2011-08-04) In the probabilistic seismic hazard analysis, the past earthquake records that can not be associated with any one of the specific faults are treated as background seismic activity. Contribution of background seismic activity to seismic hazard is generally calculated by using two different models, namely: background area source with uniform seismicity and spatially smoothed seismicity model. In this study, two case studies are carried out for a large (a country) and a small region (a province) in order to in...

Citation Formats

S. Öztürk, “Prediction of felt intensity from ground motion parameters using artificial neural network method,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences . Earthquake Studies., Middle East Technical University, 2019.