Machine learning methods are increasingly being used in the maritime domain to predict traffic anomalies and to mitigate risk, for example avoiding collision and groundingaccidents. However, most machine learning systems used for detecting such issues hasbeen trained predominately on single data sources such as vessel positioning data. Hence,it is desirable to support the means to combine different sources of data - in the trainingphase - to allow more complex models to be built. In this thesis, we propose a multi-data pipeline for accumulating, decoding, preprocessing, and merging Automatic Identification System (AIS) data with weather datato train time series based deep learning models. The pipeline comprises several REST APIsto connect and listen to the data sources, and storing and merging them using StructuredQuery Language (SQL). Specifically, the training pipeline consists of an AIS NMEA message decoder, weather data receiver, and a Postgres database for merging and storing thedata sources. Moreover, the pipeline was assessed by training a TensorFlow vRNN model.The proposed pipeline approach allows flexibility in the inclusion of new data sources toeffectively build models for the maritime domain as well as other traffic domains that usespositioning data.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-192043 |
| Date | January 2022 |
| Creators | Yahya, Sami Said |
| Publisher | Linköpings universitet, Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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