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Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting
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. M...
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| Format: | Thesis |
| Language: | English English |
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Department of Statistical Sciences
2026
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| _version_ | 1869483672128716800 |
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| access_status_str | Open Access |
| author | Harrison, Jonathan |
| author2 | Erni, Birgit |
| author_browse | Erni, Birgit Harrison, Jonathan |
| author_facet | Erni, Birgit Harrison, Jonathan |
| author_sort | Harrison, Jonathan |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/43395 |
| institution | University of Cape Town (South Africa) |
| language | English eng |
| last_indexed | 2026-07-01T04:02:43.049Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/43395 Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting Harrison, Jonathan Erni, Birgit Britz, Stefan Largier, John coastal water machine learning 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. 2026-06-26T07:58:34Z 2026-06-26T07:58:34Z 2026 2026-06-26T07:32:51Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/43395 en eng application/pdf Department of Statistical Sciences Faculty of Science University of Cape Town |
| spellingShingle | coastal water machine learning Harrison, Jonathan Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting |
| thesis_degree_str | Master's |
| title | Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting |
| title_full | Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting |
| title_fullStr | Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting |
| title_full_unstemmed | Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting |
| title_short | Coastal water level prediction: a comparative study of statistical and machine learning techniques for time series forecasting |
| title_sort | coastal water level prediction a comparative study of statistical and machine learning techniques for time series forecasting |
| topic | coastal water machine learning |
| url | http://hdl.handle.net/11427/43395 |
| work_keys_str_mv | AT harrisonjonathan coastalwaterlevelpredictionacomparativestudyofstatisticalandmachinelearningtechniquesfortimeseriesforecasting |