BibTex Citation Data :
@article{BULOMA70748, author = {Yosepine Fa'u and Widodo Setiyo Pranowo and Mario Putra Suhana and Subekti Mujiasih and Rahaden Bagas Hatmaja and Herlina Ika Ratnawati and Dony Apdillah}, title = {Analysis of Wind Characteristics and Sea Surface Elevation Dynamics in Coastal Waters of Mantang Island, Bintan Regency, Indonesia}, journal = {Buletin Oseanografi Marina}, volume = {14}, number = {2}, year = {2025}, keywords = {Admiralty; Wind; Least Square; Tides; Bintan Island}, abstract = { Geographically, Mantang Island is situated between the Malacca Strait, Natuna Sea, and Karimata Strait, and is exposed to the open sea, which influences the oceanographic dynamics of the region. The island’s residents are heavily dependent on the sea for their livelihoods, making wind and sea tides crucial for meeting their daily needs. Consequently, this study aimed to measure wind data and sea surface elevation over a 30-day period, with the results visualized using a wind rose diagram. The specific objectives were: 1) to calculate harmonic constants using both the Least Squares and Admiralty methods to obtain FormZahl numbers, 2) to determine the characteristics of sea surface elevation based on each method, and 3) to analyze the relationship between sea surface elevation and wind speed. The findings revealed that the wind in the waters surrounding Mantang Island was primarily influenced by the monsoon, blowing from the west with maximum speeds ranging from 5.70 to 8.80 m/s. Each calculation method produced varying values for sea surface elevation, including Zo, HHWL, LLWL, MHWL, and MLWL, with respective values of 11.99 m, 15.9 m, 8 m, 17.9 m, and 6 m. FormZahl number calculations yielded values of 1.25 and 1.03 using the Least Squares and Admiralty methods, respectively. Despite the differences in the results, both methods indicated a mixed semi-diurnal tidal pattern. To examine the relationship between wind and sea surface elevation, a 6th-order polynomial regression analysis was performed. The analysis revealed a weak correlation, with a coefficient of determination (R²) of 0.21 and a Root Mean Square Error (RMSE) of 0.30. These values suggest that the model’s predictions were relatively close to actual field conditions. }, issn = {2550-0015}, pages = {267--276} doi = {10.14710/buloma.v14i2.70748}, url = {https://ejournal.undip.ac.id/index.php/buloma/article/view/70748} }
Refworks Citation Data :
Geographically, Mantang Island is situated between the Malacca Strait, Natuna Sea, and Karimata Strait, and is exposed to the open sea, which influences the oceanographic dynamics of the region. The island’s residents are heavily dependent on the sea for their livelihoods, making wind and sea tides crucial for meeting their daily needs. Consequently, this study aimed to measure wind data and sea surface elevation over a 30-day period, with the results visualized using a wind rose diagram. The specific objectives were: 1) to calculate harmonic constants using both the Least Squares and Admiralty methods to obtain FormZahl numbers, 2) to determine the characteristics of sea surface elevation based on each method, and 3) to analyze the relationship between sea surface elevation and wind speed. The findings revealed that the wind in the waters surrounding Mantang Island was primarily influenced by the monsoon, blowing from the west with maximum speeds ranging from 5.70 to 8.80 m/s. Each calculation method produced varying values for sea surface elevation, including Zo, HHWL, LLWL, MHWL, and MLWL, with respective values of 11.99 m, 15.9 m, 8 m, 17.9 m, and 6 m. FormZahl number calculations yielded values of 1.25 and 1.03 using the Least Squares and Admiralty methods, respectively. Despite the differences in the results, both methods indicated a mixed semi-diurnal tidal pattern. To examine the relationship between wind and sea surface elevation, a 6th-order polynomial regression analysis was performed. The analysis revealed a weak correlation, with a coefficient of determination (R²) of 0.21 and a Root Mean Square Error (RMSE) of 0.30. These values suggest that the model’s predictions were relatively close to actual field conditions.
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