TY - GEN
T1 - The Grid-Based Spatial ARIMA Model
AU - Pongto, Ratchanont
AU - Wiwattanaphon, Nopparat
AU - Lekpong, Peerapon
AU - Lawawirojwong, Siam
AU - Srisonphan, Siwapon
AU - Kee, Kerk F.
AU - Jitkajornwanich, Kulsawasd
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - 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 7Â h prior. This configuration was found to be the best combination.
AB - 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 7Â h prior. This configuration was found to be the best combination.
KW - ARIMA
KW - Big data
KW - GNOME
KW - HF radar
KW - Ocean surface current
KW - Spatio-temporal data mining
UR - http://www.scopus.com/inward/record.url?scp=85065924046&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-19861-9_3
DO - 10.1007/978-3-030-19861-9_3
M3 - Conference contribution
AN - SCOPUS:85065924046
SN - 9783030198602
T3 - Advances in Intelligent Systems and Computing
SP - 26
EP - 36
BT - Recent Advances in Information and Communication Technology 2019 - Proceedings of the 15th International Conference on Computing and Information Technology IC2IT 2019
A2 - Unger, Herwig
A2 - Boonyopakorn, Pongsarun
A2 - Meesad, Phayung
A2 - Sodsee, Sunantha
PB - Springer-Verlag
Y2 - 4 July 2019 through 5 July 2019
ER -