Artificial Intelligence Based Prediction of Seawater Level: A Case Study for Bosphorus Strait
Sea level prediction is an important phenomenon for making reliable oceanographic and ship traffic management decisions especially for Bosphorus Strait that has no permanent sea level measurement stations due to high cost. This study presents artificial intelligence (AI) techniques, such as Artifici...
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| Format: | Article |
| Language: | English |
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Ram Arti Publishers
2021-10-01
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| Series: | International Journal of Mathematical, Engineering and Management Sciences |
| Subjects: | |
| Online Access: | https://www.ijmems.in/cms/storage/app/public/uploads/volumes/75-IJMEMS-21-0080-6-5-1242-1254-2021.pdf |
| _version_ | 1818755914223583232 |
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| author | Yavuz Karsavran Tarkan Erdik |
| author_facet | Yavuz Karsavran Tarkan Erdik |
| author_sort | Yavuz Karsavran |
| collection | DOAJ |
| description | Sea level prediction is an important phenomenon for making reliable oceanographic and ship traffic management decisions especially for Bosphorus Strait that has no permanent sea level measurement stations due to high cost. This study presents artificial intelligence (AI) techniques, such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVM) to predict the seawater level in the Bosphorus Strait. In addition, the Multiple Linear Regression model (MLR) is constructed and employed as a benchmark. The dataset employed in developing the models are wind speed, atmospheric pressure, water surface salinity, and temperature data, which were measured between September 2004 and January 2006. The results reveal that all ANN and SVM models outperform MLR and can predict the water levels quite accurately. ANN has a better performance than SVM for predicting sea level in the Bosphorus by coefficient of correlation (R) = 0.76 and root mean square error (RMSE) = 0.059. Moreover, the influence of the Danube River discharge in the prediction is investigated in the present study. The discharge of the Danube River by the lag time of 70 days yields the highest performance on ANN by increasing R to 0.82 and decreasing RMSE to 0.048. |
| first_indexed | 2024-12-18T05:46:43Z |
| format | Article |
| id | doaj.art-7a3afcb2448d4323a50593e0382f8b3e |
| institution | Directory Open Access Journal |
| issn | 2455-7749 |
| language | English |
| last_indexed | 2024-12-18T05:46:43Z |
| publishDate | 2021-10-01 |
| publisher | Ram Arti Publishers |
| record_format | Article |
| series | International Journal of Mathematical, Engineering and Management Sciences |
| spelling | doaj.art-7a3afcb2448d4323a50593e0382f8b3e2022-12-21T21:19:01ZengRam Arti PublishersInternational Journal of Mathematical, Engineering and Management Sciences2455-77492021-10-01651242125410.33889/IJMEMS.2021.6.5.075Artificial Intelligence Based Prediction of Seawater Level: A Case Study for Bosphorus StraitYavuz Karsavran0Tarkan Erdik1Hydraulics and Water Resources Department, Istanbul Technical University, Maslak, Istanbul, TurkeyHydraulics and Water Resources Department, Istanbul Technical University, Maslak, Istanbul, TurkeySea level prediction is an important phenomenon for making reliable oceanographic and ship traffic management decisions especially for Bosphorus Strait that has no permanent sea level measurement stations due to high cost. This study presents artificial intelligence (AI) techniques, such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVM) to predict the seawater level in the Bosphorus Strait. In addition, the Multiple Linear Regression model (MLR) is constructed and employed as a benchmark. The dataset employed in developing the models are wind speed, atmospheric pressure, water surface salinity, and temperature data, which were measured between September 2004 and January 2006. The results reveal that all ANN and SVM models outperform MLR and can predict the water levels quite accurately. ANN has a better performance than SVM for predicting sea level in the Bosphorus by coefficient of correlation (R) = 0.76 and root mean square error (RMSE) = 0.059. Moreover, the influence of the Danube River discharge in the prediction is investigated in the present study. The discharge of the Danube River by the lag time of 70 days yields the highest performance on ANN by increasing R to 0.82 and decreasing RMSE to 0.048.https://www.ijmems.in/cms/storage/app/public/uploads/volumes/75-IJMEMS-21-0080-6-5-1242-1254-2021.pdfseawater level predictionartificial intelligenceannsvmbosphorus straitdanube river |
| spellingShingle | Yavuz Karsavran Tarkan Erdik Artificial Intelligence Based Prediction of Seawater Level: A Case Study for Bosphorus Strait International Journal of Mathematical, Engineering and Management Sciences seawater level prediction artificial intelligence ann svm bosphorus strait danube river |
| title | Artificial Intelligence Based Prediction of Seawater Level: A Case Study for Bosphorus Strait |
| title_full | Artificial Intelligence Based Prediction of Seawater Level: A Case Study for Bosphorus Strait |
| title_fullStr | Artificial Intelligence Based Prediction of Seawater Level: A Case Study for Bosphorus Strait |
| title_full_unstemmed | Artificial Intelligence Based Prediction of Seawater Level: A Case Study for Bosphorus Strait |
| title_short | Artificial Intelligence Based Prediction of Seawater Level: A Case Study for Bosphorus Strait |
| title_sort | artificial intelligence based prediction of seawater level a case study for bosphorus strait |
| topic | seawater level prediction artificial intelligence ann svm bosphorus strait danube river |
| url | https://www.ijmems.in/cms/storage/app/public/uploads/volumes/75-IJMEMS-21-0080-6-5-1242-1254-2021.pdf |
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