Open AccessAutomatic Identification System based Fishing Trajectory Data Preprocessing Method using Map Reduce
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1067.0782s619
Subject(s) - automatic identification system , trajectory , computer science , key (lock) , fishing , identification (biology) , reducer , preprocessor , data pre processing , data mining , real time computing , engineering , artificial intelligence , ecology , physics , botany , computer security , civil engineering , astronomy , biology
Many countries use vessel monitoring system (VMS) data to monitor their fishery activities. However, VMS data is limited in terms of distinguishing operations involving illegal fishing gear. Recently introduced automatic identification system (AIS) data is advantageous for tracking fishing ship behaviors.AIS data include various types of information about a ship, such as its state of navigation and its broadcast rate on the radio channel. We interpolate AIS trajectory data with a regular time interval and extract the ship velocity and course change data for fishing ship gear classification. To simplify and condense the data, the course change index (CCI) and ship speed index (SSI) are applied to the ship velocity and course data. The proposed mapper combines CCIs and SSIs into key words, while the proposed reducer collects fishing ship gear type values that are of the same key.By using the proposed key-value dataset from the MapReduce procedure, we can classify fishing gear type. We evaluated the performance of the proposed model by using a test dataset. The results showed that the proposed model achieved 76.2% accuracy in the classification of fishing ship trajectories against the test dataset.