Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting
| dc.contributor.advisor | Erni, Birgit | |
| dc.contributor.advisor | Britz, Stefan | |
| dc.contributor.advisor | Largier, John | |
| dc.contributor.author | Harrison, Jonathan | |
| dc.date.accessioned | 2026-06-26T07:58:34Z | |
| dc.date.available | 2026-06-26T07:58:34Z | |
| dc.date.issued | 2026 | |
| dc.date.updated | 2026-06-26T07:32:51Z | |
| dc.description.abstract | Time series analysis provides powerful tools for predicting future trends, outcomes, and events. The application of these tools to coastal water level forecasting generates insightful predictions for operational use in flood management, as well as a deeper understanding of the influencing factors. Many existing models and projects focus on long-term trends in coastal water levels particularly in terms of climate change and global warming. This project investigated the application of time series analysis with exogenous meteorological variables to the task of generating accurate short-term (≤ 96 hour) forecasts of coastal water levels in a manner that is compatible with real-time monitoring for use in operational flood management. Traditional statistical methods, including regression, autoregressive integrated moving average (ARIMA), and generalised additive models, were compared alongside machine learning methods including extreme gradient boosting, support vector machines, and long short-term memory networks. Extreme gradient boosting with 24-hour of lagged input features was found to have the greatest overall test accuracy and stable predictions over the 96-hour forecast horizon. ARIMA models were the most accurate at predicting water levels in the positive stage (during high-tide). The exogenous meteorological variables contributed significantly to the models' ability to predict the water level. | |
| dc.identifier.apacitation | Harrison, J. (2026). <i>Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting</i>. (). University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/43395 | en_ZA |
| dc.identifier.chicagocitation | Harrison, Jonathan. <i>"Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting."</i> ., University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2026. http://hdl.handle.net/11427/43395 | en_ZA |
| dc.identifier.citation | Harrison, J. 2026. Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting. . University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. http://hdl.handle.net/11427/43395 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Harrison, Jonathan AB - Time series analysis provides powerful tools for predicting future trends, outcomes, and events. The application of these tools to coastal water level forecasting generates insightful predictions for operational use in flood management, as well as a deeper understanding of the influencing factors. Many existing models and projects focus on long-term trends in coastal water levels particularly in terms of climate change and global warming. This project investigated the application of time series analysis with exogenous meteorological variables to the task of generating accurate short-term (≤ 96 hour) forecasts of coastal water levels in a manner that is compatible with real-time monitoring for use in operational flood management. Traditional statistical methods, including regression, autoregressive integrated moving average (ARIMA), and generalised additive models, were compared alongside machine learning methods including extreme gradient boosting, support vector machines, and long short-term memory networks. Extreme gradient boosting with 24-hour of lagged input features was found to have the greatest overall test accuracy and stable predictions over the 96-hour forecast horizon. ARIMA models were the most accurate at predicting water levels in the positive stage (during high-tide). The exogenous meteorological variables contributed significantly to the models' ability to predict the water level. DA - 2026 DB - OpenUCT DP - University of Cape Town KW - coastal water KW - machine learning LK - https://open.uct.ac.za PB - University of Cape Town PY - 2026 T1 - Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting TI - Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting UR - http://hdl.handle.net/11427/43395 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/43395 | |
| dc.identifier.vancouvercitation | Harrison J. Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting. []. University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2026 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/43395 | en_ZA |
| dc.language.iso | en | |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Statistical Sciences | |
| dc.publisher.faculty | Faculty of Science | |
| dc.publisher.institution | University of Cape Town | |
| dc.subject | coastal water | |
| dc.subject | machine learning | |
| dc.title | Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting | |
| dc.type | Thesis / Dissertation | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |