{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T02:43:20Z","timestamp":1791859400008,"version":"4.3.4"},"reference-count":57,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2007,2]]},"abstract":"<jats:p>Neural networks are investigated for predicting the magnitude of the largest seismic event in the following month based on the analysis of eight mathematically computed parameters known as seismicity indicators. The indicators are selected based on the Gutenberg-Richter and characteristic earthquake magnitude distribution and also on the conclusions drawn by recent earthquake prediction studies. Since there is no known established mathematical or even empirical relationship between these indicators and the location and magnitude of a succeeding earthquake in a particular time window, the problem is modeled using three different neural networks: a feed-forward Levenberg-Marquardt backpropagation (LMBP) neural network, a recurrent neural network, and a radial basis function (RBF) neural network. Prediction accuracies of the models are evaluated using four different statistical measures: the probability of detection, the false alarm ratio, the frequency bias, and the true skill score or R score. The models are trained and tested using data for two seismically different regions: Southern California and the San Francisco bay region. Overall the recurrent neural network model yields the best prediction accuracies compared with LMBP and RBF networks. While at the present earthquake prediction cannot be made with a high degree of certainty this research provides a scientific approach for evaluating the short-term seismic hazard potential of a region.<\/jats:p>","DOI":"10.1142\/s0129065707000890","type":"journal-article","created":{"date-parts":[[2007,3,29]],"date-time":"2007-03-29T10:20:37Z","timestamp":1175163637000},"page":"13-33","source":"Crossref","is-referenced-by-count":244,"title":["NEURAL NETWORK MODELS FOR EARTHQUAKE MAGNITUDE PREDICTION USING MULTIPLE SEISMICITY INDICATORS"],"prefix":"10.1142","volume":"17","author":[{"given":"ASHIF","family":"PANAKKAT","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University, Columbus, Ohio 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"HOJJAT","family":"ADELI","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University, Columbus, Ohio 43210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1111\/0885-9507.00219"},{"key":"rf2","volume-title":"Machine Learning-Neural Networks, Genetic Algorithms and Fuzzy System","author":"Adeli H.","year":"1995"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-947X(2000)126:6(464)"},{"key":"rf4","doi-asserted-by":"crossref","DOI":"10.1201\/9781482267686","volume-title":"Construction Scheduling, Cost Optimization, and Management \u2014 A New Model Based on Neurocomputing and Object Technologies","author":"Adeli H.","year":"2001"},{"key":"rf5","volume-title":"Wavelets in Intelligent Transportation Systems","author":"Adeli H.","year":"2005"},{"key":"rf7","doi-asserted-by":"publisher","DOI":"10.1201\/9781315214764"},{"key":"rf8","first-page":"1276","volume":"127","author":"Adeli H.","journal-title":"Journal of Structural Engineering"},{"key":"rf11","doi-asserted-by":"publisher","DOI":"10.1126\/science.229.4714.619"},{"key":"rf12","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/8.1.66"},{"key":"rf13","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198538493.001.0001","volume-title":"Neural Network for Pattern Recognition","author":"Bishop C.","year":"1995"},{"key":"rf14","doi-asserted-by":"publisher","DOI":"10.1785\/0120000733"},{"key":"rf15","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1785\/BSSA0880020564","volume":"88","author":"Brehm D.","journal-title":"Bulletin of Seismological Society of America"},{"key":"rf16","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1785\/BSSA0890010275","volume":"89","author":"Brehm D.","journal-title":"Bulletin of Seismological Society of America"},{"key":"rf17","doi-asserted-by":"publisher","DOI":"10.1090\/S0025-5718-1965-0198670-6"},{"key":"rf18","doi-asserted-by":"publisher","DOI":"10.1029\/93JB00357"},{"key":"rf19","doi-asserted-by":"crossref","first-page":"385","DOI":"10.3319\/TAO.2004.15.3.385(EP)","volume":"15","author":"Chen Y.","journal-title":"Terrestrial Atmospheric and Oceanic Sciences"},{"key":"rf22","doi-asserted-by":"publisher","DOI":"10.1029\/97JB00625"},{"key":"rf23","volume-title":"Applied Regression Analysis","author":"Draper N.","year":"1966"},{"key":"rf24","doi-asserted-by":"publisher","DOI":"10.1207\/s15516709cog1402_1"},{"key":"rf26","volume-title":"Artificial Intelligence, A Knowledge Based Approach","author":"Firebaugh M. 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