Anomaly detection in diverse sensor networks using machine learning

buir.advisorArıkan, Orhan
dc.contributor.authorAkyol, Ali Alp
dc.date.accessioned2022-02-18T11:51:36Z
dc.date.available2022-02-18T11:51:36Z
dc.date.copyright2022-01
dc.date.issued2022-01
dc.date.submitted2022-02-04
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Ph.D.): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2022.en_US
dc.descriptionIncludes bibliographical references (leaves 116-128).en_US
dc.description.abstractEarthquake precursor detection is one of the oldest research areas that has the potential of saving human lives. Recent studies have enlightened the fact that strong seismic activities and earthquakes affect the electron distribution of the ionosphere. These effects are clearly observable on the ionospheric Total Electron Content (TEC) that shall be measured by using the satellite position data of the Global Navigation Satellite System (GNSS). In this dissertation, several earthquake precursor detection techniques are proposed and their precursor detection performances are investigated on TEC data obtained from different sensor networks. First, a model based earthquake precursor detection technique is proposed to detect precursors of the earthquakes with magnitudes greater than 5 in the vicinity of Turkey. Precursor detection and TEC reliability signals are generated by using ionospheric TEC variations. These signals are thresholded to obtain earthquake precursor decisions. Earthquake precursor detections are made by using Particle Swarm Optimization (PSO) technique on these precursor decisions. Performance evaluations show that the proposed technique is able to detect 14 out of 23 earthquake precursors of magnitude larger than 5 in Richter scale while generating 8 false precursor decisions. Second, a machine learning based earthquake precursor detection technique, EQ-PD is proposed to detect precursors of the earthquakes with magnitudes greater than 4 in the vicinity of Italy. Spatial and spatio-temporal anomaly detection thresholds are obtained by using the statistics of TEC variation during seismically active times and applied on TEC variation based anomaly detection signal to form precursor decisions. Resulting spatial and spatio-temporal anomaly decisions are fed to a Support Vector Machine (SVM) classifier to generate earthquake precursor detections. When the precursor detection performance of the EQ-PD is investigated, it is observed that the technique is able to detect 22 out of 24 earthquake precursors while generating 13 false precursor decisions during 147 days of no-seismic activity. Last, a deep learning based earthquake precursor detection technique, DLPD is proposed to detect precursors of the earthquakes with magnitudes greater than 5.4 in the vicinity Anatolia region. The DL-PD technique utilizes a deep neural network with spatio-temporal Global Ionospheric Map (GIM)-TEC data estimation capabilities. GIM-TEC anomaly score is obtained by comparing GIMTEC estimates with GIM-TEC recordings. Earthquake precursor detections are generated by thresholding the GIM-TEC anomaly scores. Precursor detection performance evaluations show that DL-PD shall detect 5 out of 7 earthquake precursors while generating 1 false precursor decision during 416 days of noseismic activity.en_US
dc.description.degreePh.D.en_US
dc.description.statementofresponsibilityby Ali Alp Akyolen_US
dc.format.extentxvi, 128 leaves : illustrations (some color), charts ; 30 cm.en_US
dc.identifier.itemidB160761
dc.identifier.urihttp://hdl.handle.net/11693/77512
dc.language.isoEnglishen_US
dc.publisherBilkent Universityen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMachine learningen_US
dc.subjectDeep learningen_US
dc.subjectIonosphereen_US
dc.subjectEarthquake precursor detectionen_US
dc.subjectGlobal Navigation Satellite System (GNSS)en_US
dc.titleAnomaly detection in diverse sensor networks using machine learningen_US
dc.title.alternativeÇeşitli sensör ağlarında makine öğrenimi ile anomali tespitien_US
dc.typeThesisen_US
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