Show simple item record

dc.contributor.authorZaidi, Talha
dc.contributor.authorSamy, Asmaa
dc.contributor.authorKocatürk, Mehmet
dc.contributor.authorAteş, Hasan Fehmi
dc.date.accessioned2021-02-12T07:49:52Z
dc.date.available2021-02-12T07:49:52Z
dc.date.issued2020en_US
dc.identifier.citationZaidi, T., Samy, A., Kocatürk, M. ve Ateş, H. F. (2020). Learned vs. hand-crafted features for deep learning based aperiodic laboratory earthquake time-prediction. 28th Signal Processing and Communications Applications Conference (SIU). Gaziantep, Turkey, 5-7 October 2020. https://dx.doi.org/10.1109/SIU49456.2020.9302474en_US
dc.identifier.isbn9781728172064
dc.identifier.issn2165-0608
dc.identifier.urihttps://dx.doi.org/10.1109/SIU49456.2020.9302474
dc.identifier.urihttps://hdl.handle.net/20.500.12511/6558
dc.description.abstractEarthquakes cause the deadliest and most costly disasters among all natural hazards. Geophysicists and data scientists have spent a lot of effort, trying to predict earthquakes time, location or magnitude to minimize these disastrous effects. However, earthquakes prediction remains a challenging problem. In this paper, the generated aperiodic earthquake failures data by Los Alamos National Laboratory (LANL) were utilized to implement three models: i) random forest (RF), ii) convolutional neural network (CNN) units followed by long short-term memory (LSTM) layers, and iii) hand-crafted features combined with CNN and LSTM layers. Among all, applying a network of CNN and LSTM layers to hand-crafted features is the most accurate and the fastest model to predict the time remaining for the next earthquake. This model achieved the prediction goal with a mean absolute error (MAE) of 1.51 in 44 seconds. These results are promising compared to other models. Thus, we anticipate that our model can be improved and applied to real seismic data that can save thousands of lives and billions of dollars in infrastructure.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectEarthquakeen_US
dc.subjectDeep Learningen_US
dc.subjectRandom Forest (RF)en_US
dc.subjectConvolutional Neural Network (CNN)en_US
dc.subjectLong Short-Term Memory (LSTM)en_US
dc.titleLearned vs. hand-crafted features for deep learning based aperiodic laboratory earthquake time-predictionen_US
dc.typeconferenceObjecten_US
dc.relation.journal28th Signal Processing and Communications Applications Conference (SIU)en_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümüen_US
dc.authorid0000-0003-1744-5252en_US
dc.authorid0000-0002-6842-1528en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1109/SIU49456.2020.9302474en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record