The Grid-Based Spatial ARIMA Model: An Innovation for Short-Term Predictions of Ocean Current Patterns with Big HF Radar Data

Ratchanont Pongto, Nopparat Wiwattanaphon, Peerapon Lekpong, Siam Lawawirojwong, Siwapon Srisonphan, Kerk F. Kee, Kulsawasd Jitkajornwanich

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Marine natural disasters have direct impacts on countries as well as their residents living on and near the coast. Warning and monitoring system can aid in reducing the loss of lives in the event of a disaster. HF (high frequency) radar, an IoT-enabled ocean surface current monitoring system, implementation is one of the first attempts towards achieving this goal. Although HF systems can monitor sea current patterns in terms of speed and direction for each of the pixels of the coverage area, it fails to predict future values, which are essential to many applications such as oil-spill trajectory prediction (using the GNOME suite: General NOAA Operational Modeling Environment), water quality control and management, and optimized sea navigation. In this paper, we propose a model, called the grid-based spatial ARIMA (auto-regressive integrated moving average), to estimate the forecast values. As a result, the full potential of the HF systems can be utilized. The method considers not only observations of POI (point of interest), but also its neighboring pixels when predicting future values. The proposed method is implemented and compared with other existing approaches, including baseline, kNN, traditional ARIMA model, and LSTM (long short-term memory) techniques. The experimental results showed that our approach outperformed other methods in V comp prediction (with RMSEs of 6.23265) with a configuration of (2, 0, 1) as (p, d, q) and a historical dataset of 1 day and 7 h prior. This configuration was found to be the best combination.

Original languageEnglish
Title of host publicationRecent Advances in Information and Communication Technology 2019 - Proceedings of the 15th International Conference on Computing and Information Technology IC2IT 2019
EditorsHerwig Unger, Pongsarun Boonyopakorn, Phayung Meesad, Sunantha Sodsee
PublisherSpringer-Verlag
Pages26-36
Number of pages11
ISBN (Print)9783030198602
DOIs
StatePublished - 2020
Event15th International Conference on Computing and Information Technology, IC2IT 2019 - Bangkok, Thailand
Duration: Jul 4 2019Jul 5 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume936
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference15th International Conference on Computing and Information Technology, IC2IT 2019
CountryThailand
CityBangkok
Period07/4/1907/5/19

Keywords

  • ARIMA
  • Big data
  • GNOME
  • HF radar
  • Ocean surface current
  • Spatio-temporal data mining

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