Peak Ground Acceleration Prediction by Artificial Neural Networks for Northwestern Turkey
Three different artificial neural network (ANN) methods, namely, feed-forward back-propagation (FFBP), radial basis function (RBF), and generalized regression neural networks (GRNNs) were applied to predict peak ground acceleration (PGA). Ninety five three-component records from 15 ground motions th...
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| Veröffentlicht in: | Mathematical Problems in Engineering 2008-01, Vol.2008 (1), p.1413-1432 |
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| creator | Gunaydin, Kemal Gunaydin, Ayten |
| description | Three different artificial neural network (ANN) methods, namely, feed-forward back-propagation (FFBP), radial basis function (RBF), and generalized regression neural networks (GRNNs) were applied to predict peak ground acceleration (PGA). Ninety five three-component records from 15 ground motions that occurred in Northwestern Turkey between 1999 and 2001 were used during the applications. The earthquake moment magnitude, hypocentral distance, focal depth, and site conditions were used as inputs to estimate PGA for vertical (U-D), east-west (E-W), and north-south (N-S) directions. The direction of the maximum PGA of the three components was also added to the input layer to obtain the maximum PGA. Testing stage results of three ANN methods indicated that the FFBPs were superior to the GRNN and the RBF for all directions. The PGA values obtained from the FFBP were modified by linear regression analysis. The results showed that these modifications increased the prediction performances. |
| doi_str_mv | 10.1155/2008/919420 |
| format | Article |
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Ninety five three-component records from 15 ground motions that occurred in Northwestern Turkey between 1999 and 2001 were used during the applications. The earthquake moment magnitude, hypocentral distance, focal depth, and site conditions were used as inputs to estimate PGA for vertical (U-D), east-west (E-W), and north-south (N-S) directions. The direction of the maximum PGA of the three components was also added to the input layer to obtain the maximum PGA. Testing stage results of three ANN methods indicated that the FFBPs were superior to the GRNN and the RBF for all directions. The PGA values obtained from the FFBP were modified by linear regression analysis. The results showed that these modifications increased the prediction performances.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2008/919420</identifier><language>eng</language><publisher>New York: Hindawi Limiteds</publisher><subject>Artificial neural networks ; Back propagation networks ; Earthquake prediction ; Earthquakes ; Ground motion ; Neural networks ; Radial basis function ; Regression analysis ; Studies</subject><ispartof>Mathematical Problems in Engineering, 2008-01, Vol.2008 (1), p.1413-1432</ispartof><rights>Copyright © 2008</rights><rights>Copyright © 2008 Kemal Günaydin et al. 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Ninety five three-component records from 15 ground motions that occurred in Northwestern Turkey between 1999 and 2001 were used during the applications. The earthquake moment magnitude, hypocentral distance, focal depth, and site conditions were used as inputs to estimate PGA for vertical (U-D), east-west (E-W), and north-south (N-S) directions. The direction of the maximum PGA of the three components was also added to the input layer to obtain the maximum PGA. Testing stage results of three ANN methods indicated that the FFBPs were superior to the GRNN and the RBF for all directions. The PGA values obtained from the FFBP were modified by linear regression analysis. The results showed that these modifications increased the prediction performances.</description><subject>Artificial neural networks</subject><subject>Back propagation networks</subject><subject>Earthquake prediction</subject><subject>Earthquakes</subject><subject>Ground motion</subject><subject>Neural networks</subject><subject>Radial basis function</subject><subject>Regression analysis</subject><subject>Studies</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>BENPR</sourceid><recordid>eNqFkU1v2zAMho2iA_qxnfoHjB126OCGlCxLOgZBmw0o0hwyYDsZsi0jalIro2wE-fdT4gJDe-lFLwk8FMmXSXKDcIcoxIQBqIlGnTM4Sy5RFDwTmMvzGAPLM2T890VyFcIzAEOB6jL5s7Rmk87JD12TTuvabi2Z3vkuXZJtXH0Kq0M6pd61rnZmmy7sQCfp9542IW09pQtP_XpvQ2-pS1cDbezhc_KpNdtgv7zqdfLr4X41-5E9Ps1_zqaPmREAfaZUJSVvtEbbsNbwqrCKtY2ASoOoCrCFlqBVg5yB5kZIzSGSvBBK50bX_Dr5Nv67I_93iCOULy7EPbams34IJedcSRAigl_fgc9-oC7OViohMdda8Qh9H6GafAhk23JH7sXQoUQojx6XR4_L0eNI34702nWN2bsP4MUIG0eud_-7LxlggYgyXuVUgUeJabwRAMi3CebI48MZ_wcPTI7q</recordid><startdate>20080101</startdate><enddate>20080101</enddate><creator>Gunaydin, Kemal</creator><creator>Gunaydin, Ayten</creator><general>Hindawi Limiteds</general><general>Hindawi Publishing Corporation</general><general>Hindawi Limited</general><scope>188</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>7SM</scope></search><sort><creationdate>20080101</creationdate><title>Peak Ground Acceleration Prediction by Artificial Neural Networks for Northwestern Turkey</title><author>Gunaydin, Kemal ; 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Ninety five three-component records from 15 ground motions that occurred in Northwestern Turkey between 1999 and 2001 were used during the applications. The earthquake moment magnitude, hypocentral distance, focal depth, and site conditions were used as inputs to estimate PGA for vertical (U-D), east-west (E-W), and north-south (N-S) directions. The direction of the maximum PGA of the three components was also added to the input layer to obtain the maximum PGA. Testing stage results of three ANN methods indicated that the FFBPs were superior to the GRNN and the RBF for all directions. The PGA values obtained from the FFBP were modified by linear regression analysis. The results showed that these modifications increased the prediction performances.</abstract><cop>New York</cop><pub>Hindawi Limiteds</pub><doi>10.1155/2008/919420</doi><tpages>20</tpages><oa>free_for_read</oa></addata></record> |
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| source | Wiley Online Library Open Access; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Artificial neural networks Back propagation networks Earthquake prediction Earthquakes Ground motion Neural networks Radial basis function Regression analysis Studies |
| title | Peak Ground Acceleration Prediction by Artificial Neural Networks for Northwestern Turkey |
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