Kendini tekrarlayan derin sinir ağlarının öznitelik seçim yöntemleri ile iyileştirilmesi ve zaman serisi olarak ele alınan otomatik tanımlama sistemi verilerinde kullanımı

Yıl 2020, , 1897 - 1912, 21.07.2020

Yunus Doğan

https://doi.org/10.17341/gazimmfd.676862

Cited By: 2

Öz

Otomatik Tanımlama Sistemi (AIS), deniz taşımacılığının, çarpışma, yangın ve tehlikeli veya kirletici maddelerin dökülmesi gibi risklere sahip olması nedeniyle günümüzde zorunlu hale gelmiş gözlem ve analiz sistemidir. Literatürde, bu tehlikeli durumların önceden tespitinin yapılıp, gemilerin kontrollü ve güvenli seyahatlerini gerçekleştirmeleri için AIS verilerinin kullanıldığı temel matematiksel modellerin, istatistiksel modellerin ve makine öğrenmesi algoritmaların uygulamalarını görebilmekteyiz. Bu çalışmada AIS verileri zaman serileri bakış açısıyla ele alınmış ve geleneksel rota tahminleme modeli yanında; Bütünleşik Otoregresif Hareketli Ortalama, Çok Katmanlı Algılayıcı (ÇKA) ve Kendini Tekrarlayan Derin Sinir Ağları (KT-DSA) ile farklı modeller oluşturularak doğruluk karşılaştırmaları yapılmıştır. Ayrıca ÇKA ve KT-DSA modellerinde, öznitelik seçim tekniklerinden yararlanılarak nitelikler ağırlıklandırılmış ve bu iyileştirilmelerle yeni algoritmalar önerilmiştir. Öznitelik seçimlerinden Relief, Pearson’nun Korelasyonu, Kazanım Oranı ve Bilgi Kazanımı (BK) metotları kullanılmış ve verdikleri rota ve çarpışma tahminlemelerinin doğrulukları karşılaştırılmıştır. Bu doğruluk testlerinde kullanılmak üzere veri seti olarak belirli zamanlara ait Çanakkale Boğazı ve Marmara Denizi AIS verilerinden faydalanılmıştır. Sonuçlara bakıldığında Çanakkale Boğazı’ndaki gemilerin doğrusal bir hareket yapısına sahip olmasından dolayı tüm yaklaşımların birbirine yakın ve yüksek doğruluklara sahip olduğu gözlemlenirken, düzensiz yapısından dolayı Marmara Denizi’nde en iyi sonucu veren yaklaşımın BK ile iyileştirilmiş KT-DSA olduğu sonucuna varılmıştır.

Anahtar Kelimeler

Büyük veri, derin öğrenme, AIS verisi, öznitelik seçimi, kümeleme

Teşekkür

Bu çalışmanın testlerini yapabilmek için gerekli olan AIS verilerinin sağlanmasında yardımı olan Dokuz Eylül Üniversitesi Denizcilik Fakültesi’nden Prof. Dr. Selçuk Nas’a teşekkür ederim.

Improvement of recurrent deep neural networks algorithm by feature selection methods and its usage of automatic identification system data evaluated as time series.

Öz

Automatic Identification System (AIS) is an
observation and analysis system that has become compulsory nowadays due to the
risks of maritime transportation such as collision, fire, and spillage of
hazardous or polluting substances. In the literature, we can see the
applications of basic mathematical models, statistical models and machine
learning algorithms using AIS data in order to detect these dangers in advance
and to make controlled and safe travel of ships. In this study, AIS data have
been evaluated as time series, and accuracy comparisons have been made by being
developed different models with Autoregressive Integrated Moving Average,
Multilayer Perceptron (MLP) and Deep Recurrent Neural Networks (DRNN) beside
traditional route estimation model. In addition, feature selection techniques
have been weighted in MLP and RDNN models, and new algorithms have been
proposed with these improving. Relief, Pearson's Correlation, Gain Ratio and
Information Gain (IG) methods were used to compare the accuracy of the route
and collision estimations. In order to be used in these accuracy tests, AIS
data related into certain times of Çanakkale Strait and Marmara Sea were used.
The results showed that all the approaches were close and high accuracy due to
the linear movement of the ships in the Dardanelles. On the other hand, it has
been observed that the best approach in the Marmara Sea was the improved DRNN
with IG due to its irregular structure.

Anahtar Kelimeler

Big data, deep learning, AIS data, feature selection, clustering

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