Quantified Assessment of Shoreline Change Analysis Using Machine Learning Techniques

海岸线很可能受到气候变化的严重影响，尤其是海平面上升和风暴活动增加的影响。这可能导致海滩侵蚀的速度增加，同时影响沿海生态和社会经济。了解改变海岸线的过程，并能够预测可能发生的变化，对海岸管理来说是必不可少的，也是一项具有挑战性的任务。越来越多的海岸线形态数据提供了探索更新的“数据驱动”沿海建模方法的能力。机器学习是一种数据驱动的方法，有助于识别变量之间的复杂关系，从而提高预测的准确性。最近，海岸线变化研究已经开始实施这些方法，然而，量化不同ML方法的性能以及有效模拟海岸线变化所需的数据量的研究有限。该项目旨在使用ML模型对英国海岸线的一段进行海岸线变化分析，评估不同ML模型的性能。然后，该项目将致力于确定最佳执行SCA的数据数量限制，并为在较少“数据丰富”的区域进行ML SCA研究提供一个框架。

The shoreline is likely to be severely impacted by climatic changes, with particular concerns over the impacts of sea level rise and increased storm activity. This can lead to the rates of beach erosion increasing, impacting both coastal ecology and socio-economics alike. Developing an understanding of the processes that change shorelines and being able to predict what changes are likely to occur are imperative for coastal management and is a challenging task. The growing availability of shoreline morphology data offers the ability to explore newer 'data driven' approaches to coastal modelling. Machine Learning is a data driven approach that helps to identify complex relationships between variables, offering greater accuracies in prediction. Recently, shoreline change studies have started to implement these methods, however, there are limited studies that quantify the performance of different ML approaches, and the quantities of data needed to effectively model shoreline change. This project will aim to perform shoreline change analysis on a section of the UK coastline using ML models, assessing the performance of different ML models. The project will then aim to identify the data quantity limits to best perform SCA, and provide a framework for ML SCA studies on less 'data rich' regions.