Short-term sea level forecasting using machine learning techniques: A case study for South Africa
| dc.contributor.advisor | Smit, Albertus | |
| dc.contributor.advisor | Rautenbach, Christo | |
| dc.contributor.advisor | Vichi, Marcello | |
| dc.contributor.author | Ingreso, Maria Kristina | |
| dc.date.accessioned | 2023-03-13T11:17:23Z | |
| dc.date.available | 2023-03-13T11:17:23Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2023-02-20T12:57:40Z | |
| dc.description.abstract | Seawater levels along the South African coastline are investigated with the use of machine learning techniques. In this study, data-driven methods, which are more computationally efficient in comparison to numerical models, are applied to predict seawater levels. The open-loop NARX model was developed using the Neural Net Time Series application from the Deep Learning Toolbox 14.0 provided by MATLABĀ® (Mathworks, 2020). A total of five inputs (atmospheric pressure, mean wave period and direction, wind speed and direction) and a single output of seawater level was fed into the neural network where 70 % of the data was used for training, 15 % was used for validation and the remaining 15 % was used to test the model. Three separate storm events that occurred along the coast of South Africa were used for the final model validation. Model performance was measured using the correlation coefficient (R), the root mean square error (RMSE), the bias and the Willmott indices of correlation. It was found that, through principal component analysis (PCA), atmospheric pressure, wind speed and direction and mean wave period and direction are important physical drivers of sea level. The overall model performance was better when all five met-ocean variables were included as inputs to the model than when one or two were excluded, with R and RMSE values ranging from 0.85 to 0.99 and 4.344 to 100.5 mm, respectively. The study presented here clearly shows an effective methodology to not only demonstrate the high accuracy the model has on seawater level predictions, but also able to further investigate the importance of what each oceanic and atmospheric variable has on the seawater level. The model performance may be affected by frictional shoaling, coastally trapped waves, bathymetry and the local dynamics contributed by Agulhas Current, which were not taken account for in this study and could be incorporated in the model for future research. | |
| dc.identifier.apacitation | Ingreso, M. K. (2022). <i>Short-term sea level forecasting using machine learning techniques: A case study for South Africa</i>. (). ,Faculty of Science ,Department of Oceanography. Retrieved from http://hdl.handle.net/11427/37386 | en_ZA |
| dc.identifier.chicagocitation | Ingreso, Maria Kristina. <i>"Short-term sea level forecasting using machine learning techniques: A case study for South Africa."</i> ., ,Faculty of Science ,Department of Oceanography, 2022. http://hdl.handle.net/11427/37386 | en_ZA |
| dc.identifier.citation | Ingreso, M.K. 2022. Short-term sea level forecasting using machine learning techniques: A case study for South Africa. . ,Faculty of Science ,Department of Oceanography. http://hdl.handle.net/11427/37386 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Ingreso, Maria Kristina AB - Seawater levels along the South African coastline are investigated with the use of machine learning techniques. In this study, data-driven methods, which are more computationally efficient in comparison to numerical models, are applied to predict seawater levels. The open-loop NARX model was developed using the Neural Net Time Series application from the Deep Learning Toolbox 14.0 provided by MATLABĀ® (Mathworks, 2020). A total of five inputs (atmospheric pressure, mean wave period and direction, wind speed and direction) and a single output of seawater level was fed into the neural network where 70 % of the data was used for training, 15 % was used for validation and the remaining 15 % was used to test the model. Three separate storm events that occurred along the coast of South Africa were used for the final model validation. Model performance was measured using the correlation coefficient (R), the root mean square error (RMSE), the bias and the Willmott indices of correlation. It was found that, through principal component analysis (PCA), atmospheric pressure, wind speed and direction and mean wave period and direction are important physical drivers of sea level. The overall model performance was better when all five met-ocean variables were included as inputs to the model than when one or two were excluded, with R and RMSE values ranging from 0.85 to 0.99 and 4.344 to 100.5 mm, respectively. The study presented here clearly shows an effective methodology to not only demonstrate the high accuracy the model has on seawater level predictions, but also able to further investigate the importance of what each oceanic and atmospheric variable has on the seawater level. The model performance may be affected by frictional shoaling, coastally trapped waves, bathymetry and the local dynamics contributed by Agulhas Current, which were not taken account for in this study and could be incorporated in the model for future research. DA - 2022_ DB - OpenUCT DP - University of Cape Town KW - Oceanography LK - https://open.uct.ac.za PY - 2022 T1 - Short-term sea level forecasting using machine learning techniques: A case study for South Africa TI - Short-term sea level forecasting using machine learning techniques: A case study for South Africa UR - http://hdl.handle.net/11427/37386 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/37386 | |
| dc.identifier.vancouvercitation | Ingreso MK. Short-term sea level forecasting using machine learning techniques: A case study for South Africa. []. ,Faculty of Science ,Department of Oceanography, 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/37386 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Oceanography | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | Oceanography | |
| dc.title | Short-term sea level forecasting using machine learning techniques: A case study for South Africa | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |