{"id":188,"date":"2019-07-09T14:14:00","date_gmt":"2019-07-09T12:14:00","guid":{"rendered":"https:\/\/softwaredev.de\/?p=188"},"modified":"2021-01-18T09:14:56","modified_gmt":"2021-01-18T08:14:56","slug":"machine-learning-earthquake-prediction","status":"publish","type":"post","link":"https:\/\/softwaredev.de\/index.php\/2019\/07\/09\/machine-learning-earthquake-prediction\/","title":{"rendered":"Machine Learning &#8211; Earthquake Prediction"},"content":{"rendered":"\n<h3 class=\"wp-block-heading\">Einf\u00fchrung<\/h3>\n\n\n\n<p>Im Rahmen meiner Zertifizierung zum Machine Learning\nEngineer habe ich als Abschlussprojekt die Vorhersage von Erdbeben gew\u00e4hlt.<\/p>\n\n\n\n<p>Ziel des Projektes ist es ein Machine Learning Model zu trainieren, dass mit seismischen Daten eine pr\u00e4zise Vorhersage machen kann wann ein Erdbeben auftreten wird.<\/p>\n\n\n\n<p>Das Projekt wurde in der Programmierspache Python mit Tensorflow\/Keras implementiert.<\/p>\n\n\n\n<p>Details zu diesem Projekt auf <a href=\"https:\/\/github.com\/tobiassteidle\/MLND_Capstone\">Github.<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Daten Analyse<\/h3>\n\n\n\n<p>Die Grundlage f\u00fcr das Training ist ein Datensatz in Form eine CSV Datei. Die Datei besteht aus 2 Spalten \u201eacoustic_data\u201c (Amplitude des seismischen Signals) und \u201etime_to_failure\u201c (Zeit bis zum eintreten des Erdbebens).<\/p>\n\n\n\n<p>Hier ein Beispiel der Datei:<\/p>\n\n\n\n<figure class=\"wp-block-image is-resized\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/trainings_data.png\" alt=\"\" class=\"wp-image-192\" width=\"201\" height=\"157\" srcset=\"https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/trainings_data.png 802w, https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/trainings_data-300x234.png 300w, https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/trainings_data-768x599.png 768w\" sizes=\"auto, (max-width: 201px) 100vw, 201px\" \/><\/figure>\n\n\n\n<p>Eine visuelle Darstellung des akustischen Signals sieht so aus:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"214\" src=\"https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/plot_earthquake-1024x214.png\" alt=\"\" class=\"wp-image-194\" srcset=\"https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/plot_earthquake-1024x214.png 1024w, https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/plot_earthquake-300x63.png 300w, https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/plot_earthquake-768x160.png 768w, https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/plot_earthquake.png 1920w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Metrik<\/h3>\n\n\n\n<p>Die Metrik um zu pr\u00fcfen ob das Model wirklich lernt ist der Mean Absolute Error (MAE). Der MEA wird mit folgender Formel berechnet:<\/p>\n\n\n\n<figure class=\"wp-block-image is-resized\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/mea.png\" alt=\"\" class=\"wp-image-197\" width=\"341\" height=\"137\" srcset=\"https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/mea.png 455w, https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/mea-300x120.png 300w\" sizes=\"auto, (max-width: 341px) 100vw, 341px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image is-resized\"><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/mea2.png\" alt=\"\" class=\"wp-image-199\" width=\"293\" height=\"74\" srcset=\"https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/mea2.png 585w, https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/mea2-300x75.png 300w\" sizes=\"auto, (max-width: 293px) 100vw, 293px\" \/><\/figure>\n\n\n\n<p>F\u00fcr jeden Datensatz des Testdatensatzes wird der Vorhersagefehler berechnet und in einen positiven Wert konvertiert indem man den Absolutwert f\u00fcr jeden Fehler nimmt. Abschlie\u00dfend wird der Mittelwert f\u00fcr alle aufgezeichneten Fehler berechnet. Das ist dann der MAE.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Features<\/h3>\n\n\n\n<p>Als Features f\u00fcr das Training habe ich mich f\u00fcr <a href=\"https:\/\/de.wikipedia.org\/wiki\/Mel_Frequency_Cepstral_Coefficients\">MFCC (Mel Frequency Cepstral Coefficients)<\/a> entschieden. Der MFCC f\u00fchrt zu einer kompakten Darstellung des Frequenzspektrums. In der finalen Version wurden 10 Koeffizienten gew\u00e4hlt bei einer Abtastrate von 44100 Hz.<br>Hier die visuelle Darstellung des MFCC:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"264\" src=\"https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/mfcc-1024x264.png\" alt=\"\" class=\"wp-image-205\" srcset=\"https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/mfcc-1024x264.png 1024w, https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/mfcc-300x77.png 300w, https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/mfcc-768x198.png 768w, https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/mfcc.png 1762w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>Die daraus resultierenden 10 Features sehen visuell so aus:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"270\" src=\"https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/mfcc_features-1024x270.png\" alt=\"\" class=\"wp-image-206\" srcset=\"https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/mfcc_features-1024x270.png 1024w, https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/mfcc_features-300x79.png 300w, https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/mfcc_features-768x202.png 768w, https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/mfcc_features.png 1762w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>Da einige Features relativ gleich im Verlauf sind (z.B. \u201emfcc_2\u201c und \u201emfcc_3\u201c) habe ich entschieden per <a href=\"https:\/\/de.wikipedia.org\/wiki\/Hauptkomponentenanalyse\">Principal Component Analysis (PCA)<\/a> die Anzahl der Features von 10 auf 6 zu reduzieren. <\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Training<\/h3>\n\n\n\n<p>Mit den verbleibenden Features wurde ein <a href=\"https:\/\/de.wikipedia.org\/wiki\/Rekurrentes_neuronales_Netz\">Rekurrentes neuronales Netz (RNN) trainiert<\/a>.<br>Im Ergebnis entstand ein Model das zur vorhersage von Erdbeben im Laborumfeld geeignet ist.<\/p>\n\n\n\n<p>Das RNN ist wie folgt aufgebaut:<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"630\" src=\"https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/rnn-1024x630.png\" alt=\"\" class=\"wp-image-210\" srcset=\"https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/rnn-1024x630.png 1024w, https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/rnn-300x185.png 300w, https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/rnn-768x473.png 768w, https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/07\/rnn.png 1727w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Einf\u00fchrung Im Rahmen meiner Zertifizierung zum Machine Learning Engineer habe ich als Abschlussprojekt die Vorhersage von Erdbeben gew\u00e4hlt. Ziel des Projektes ist es ein Machine Learning Model zu trainieren, dass mit seismischen Daten eine pr\u00e4zise Vorhersage machen kann wann ein Erdbeben auftreten wird. Das Projekt wurde in der Programmierspache Python mit Tensorflow\/Keras implementiert. Details zu [&#8230;]<\/p>\n<p><a class=\"btn btn-secondary understrap-read-more-link\" href=\"https:\/\/softwaredev.de\/index.php\/2019\/07\/09\/machine-learning-earthquake-prediction\/\">Weiterlesen\u00a0\u2026<span class=\"screen-reader-text\"> from Machine Learning &#8211; Earthquake Prediction<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[36,12],"tags":[],"class_list":["post-188","post","type-post","status-publish","format-standard","hentry","category-blog","category-machine-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.0 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Machine Learning - Earthquake Prediction - Tobias Steidle Software Development<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/softwaredev.de\/index.php\/2019\/07\/09\/machine-learning-earthquake-prediction\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning - Earthquake Prediction - Tobias Steidle Software Development\" \/>\n<meta property=\"og:description\" content=\"Einf\u00fchrung Im Rahmen meiner Zertifizierung zum Machine Learning Engineer habe ich als Abschlussprojekt die Vorhersage von Erdbeben gew\u00e4hlt. Ziel des Projektes ist es ein Machine Learning Model zu trainieren, dass mit seismischen Daten eine pr\u00e4zise Vorhersage machen kann wann ein Erdbeben auftreten wird. Das Projekt wurde in der Programmierspache Python mit Tensorflow\/Keras implementiert. 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