TY - GEN
T1 - Estimation the Height of Tsunami Waves in the Southern Island of Java Using Ensemble Kalman Filter Method
AU - Putra, Firdaus Priyatno
AU - Apriliani, Erna
AU - Handoko, Eko Yuli
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Tsunami is an ocean wave that has large speed and energy. It can destroy the shoreline area. In Indonesia, tsunami has been happened 187 times based on BNBP's data. Tsunami is caused by 3 factors: earthquake, volcanic eruption, and plate friction. We present an estimation of tsunami wave height in the shoreline area using Ensemble Kalman filter. Ensemble Kalman filter is one example of data assimilation method which is combining model system and measurement data to get best approximation. The model of tsunami based on shallow water equation with discretize first using Runge Kutta 4th order for easy in computing. The data based on data measurement in Cilacap station when Pangandaran's tsunami happened in 2006, in Serang station, and in Banten station when Sunda's strait tsunami happened in 2018. In addition, we add the relation between sea floor level and tsunami wave height. The result of estimation error in station Cilacap is 2.88%, in Serang station is 1.06%, and in Banten station is 1.01%. This indicates that Ensemble Kalman filter method is working properly to approximate shoreline area. Based on result the more ensemble number, the fewer error and the more computation time. The faster tsunami arrives to shoreline area and the more slope the topography in shoreline area, the higher waves tsunami happen.
AB - Tsunami is an ocean wave that has large speed and energy. It can destroy the shoreline area. In Indonesia, tsunami has been happened 187 times based on BNBP's data. Tsunami is caused by 3 factors: earthquake, volcanic eruption, and plate friction. We present an estimation of tsunami wave height in the shoreline area using Ensemble Kalman filter. Ensemble Kalman filter is one example of data assimilation method which is combining model system and measurement data to get best approximation. The model of tsunami based on shallow water equation with discretize first using Runge Kutta 4th order for easy in computing. The data based on data measurement in Cilacap station when Pangandaran's tsunami happened in 2006, in Serang station, and in Banten station when Sunda's strait tsunami happened in 2018. In addition, we add the relation between sea floor level and tsunami wave height. The result of estimation error in station Cilacap is 2.88%, in Serang station is 1.06%, and in Banten station is 1.01%. This indicates that Ensemble Kalman filter method is working properly to approximate shoreline area. Based on result the more ensemble number, the fewer error and the more computation time. The faster tsunami arrives to shoreline area and the more slope the topography in shoreline area, the higher waves tsunami happen.
KW - Ensemble Kalman filter
KW - Runge Kutta 4 order
KW - Tsunami
KW - varying depth
UR - http://www.scopus.com/inward/record.url?scp=85085866072&partnerID=8YFLogxK
U2 - 10.1109/ICEEI47359.2019.8988859
DO - 10.1109/ICEEI47359.2019.8988859
M3 - Conference contribution
AN - SCOPUS:85085866072
T3 - Proceedings of the International Conference on Electrical Engineering and Informatics
SP - 477
EP - 482
BT - Proceeding of 2019 International Conference on Electrical Engineering and Informatics, ICEEI 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Electrical Engineering and Informatics, ICEEI 2019
Y2 - 9 July 2019 through 10 July 2019
ER -