Yapay sinir ağı ile deprem şiddeti tahmini: Farklı ağ tasarımlarının ve eğitim algoritmalarının incelenmesi

Bu çalışmada, ileri beslemeli geri yayılımlı bir yapay sinir ağı ile depremin büyüklüğü, derinliği ve afetzedelerin merkez üssüne olan uzaklıklarına bağlı olarak deprem şiddeti tahmini yapılmıştır. Bu kapsamda, Amerika Birleşik Devletleri Jeoloji Araştırmaları Kurumu’nun veri tabanında yer alan ve önemli depremler olarak adlandırılan depremlere ilişkin bilgiler yapay sinir ağının girdisi olarak kullanılmıştır. Farklı yapay sinir ağı tasarımları için deprem şiddeti tahmin edilerek uygun bir ağ tasarımı elde edilmiştir. Ardından söz konusu uygun ağ tasarımı için farklı eğitim algoritmaları kullanılarak ağ eğitilmiş ve bu algoritmalar arasından en uygun eğitim yöntemi belirlenmiştir. Farklı ağ tasarımlarının ve eğitim algoritmalarının performansları, ortalama karesel hata ve korelasyon katsayısı cinsinden analiz edilmiştir. Performans parametrelerinin ortalaması açısından, iki gizli katman ve her bir katmanda sırasıyla beş ve on gizli nöronun bulunduğu ağ yapısı en uygun tasarım olarak belirlenmiştir. Söz konusu ağ yapısı için Bayes Düzenlemesi ile Levenberg-Marquardt eğitim algoritmasının kullanıldığı durumda performans parametreleri açısından en iyi sonuçlar gözlenmiştir.

Anahtar Kelimeler:

Afet operasyonları yönetimi, Afet yardım operasyonları, Makine öğrenmesi, Yapay sinir ağları, Deprem şiddeti tahmini

Earthquake intensity estimation via an artificial neural network: Examination of different network designs and training algorithms

In this study, using a multi-layer feed-forward artificial neural network, we estimate earthquake intensity based on the magnitude and the depth of an earthquake and the distance of the disaster victims from the epicenter of the earthquake. In this context, we use significant earthquakes database of the United States Geological Survey as the inputs of the artificial neural network. We first determine an appropriate network design by estimating earthquake intensity with different artificial neural network designs and then the best training algorithm for the appropriate network design by evaluating different algorithms for the corresponding network design. These analyses are performed in terms of the mean square error and correlation coefficient. In terms of the average performance parameters, the network structure with two hidden layers and five and ten hidden neurons in each respective layer is determined as the most appropriate design. We observe the best results in terms of performance parameters by using the Levenberg-Marquardt training algorithm with Bayesian Regularization for the corresponding network structure.

Keywords:

Disaster operations management, Disaster relief operations, Machine learning, Artificial neural networks, Earthquake intensity estimation,

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