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Ocean surface current prediction based on HF radar observations using trajectory-oriented association rule mining
Author(s)
Jitkajornwanich, Kulsawasd
Vateekul, Peerapon
Gupta, Upa
Kormongkolkul, Teeranai
Jirakittayakorn, Arnon
Lawawirojwong, Siam
Srisonphan, Siwapon
Date Issued
July 1, 2017
Type
Conference Paper
Abstract
HF (high frequency) coastal radar system is used to capture the surface current behavior - in terms of velocity and direction - in the ocean near the coast. 18 HF coastal radar stations were implemented along the Gulf of Thailand in order to monitor for disasters (e.g., Tsunami) as well as relevant risks. The HF systems are also to serve other life-critical applications, such as water quality control and monitoring, chemical spill backtracking, and marine navigation. However, not all the applications can benefit from this near-real-time HF data; some applications in different domains require forecast values. The examples include search-and-rescue system and hazardous materials spill trajectory prediction. Therefore, in this paper, we propose a predictive model for future current data based on historical HF coastal radar data sets, utilizing association rule mining combined with an object dispersion concept. So, the full potential of HF radar systems can be exploited. The spatial and temporal dimensions are taken into account when designing our predictive system, which consists of two phases: ocean surface current track formulation and spatiooral association rule mining. The experiments are performed on a two-year HF radar dataset (2014-2015) using Google Cloud Platform. The resulting forecast current values: velocity and direction are then compared with testing datasets (using 10-fold cross validation) of the actual recorded values and evaluated based on percentage accuracy and RMSE, respectively.
Citation
Proceedings 2017 IEEE International Conference on Big Data Big Data 2017, 2018-January, 4293-4300, 2017