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Author: search.filters.author.Jirakittayakorn, ArnonSubject: search.filters.subject.HF radar

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    Temporal kNN for short-Term ocean current prediction based on HF radar observations
    (2017-09-05)
    Jirakittayakorn, Arnon
    ;
    Kormongkolkul, Teeranai
    ;
    Vateekul, Peerapon
    ;
    Jitkajornwanich, Kulsawasd
    ;
    Lawawirojwong, Siam
    Ocean surface current prediction is at the core of various marine operational routines, including disaster monitoring, oil-spill backtracking, sea navigation and search-And-rescue operations. More accurate prediction can yield significant improvement to the overall system. Most existing short-Term prediction methods applied numerical models based on physical processes. In this paper, we propose an alternative approach in predicting the surface current by utilizing temporal k-nearest-neighbor technique, which can predict the future surface current up to 24 hours in advance. Our model incorporates several pre-processing methods, e.g. feature extraction and data transformation, in order to capture the seasonal and temporal characteristics of the HF (high frequency) radar observation data. The developed model was implemented, validated and compared with the existing models using the same historical datasets collected from the HF coastal radar stations located along the Gulf of Thailand. Our experimental results indicate that the proposed model can achieve the highest accuracy among all methods, including ARIMA, exponential smoothing, and LSTM; and satisfy the oil-spill backtracking application requirements. In addition, we found that our system requires little to none maintenance and can easily be adapted to other coastal radar locations where the amount of historical HF radar observations is limited.
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    Ocean surface current prediction based on HF radar observations using trajectory-oriented association rule mining
    (2017-07-01)
    Jitkajornwanich, Kulsawasd
    ;
    Vateekul, Peerapon
    ;
    Gupta, Upa
    ;
    Kormongkolkul, Teeranai
    ;
    Jirakittayakorn, Arnon
    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.