Artificial Intelligence-assisted decadal scale beach change forecasting

英国每年的洪水和海岸侵蚀损失估计为5.4亿英镑。与气候变化相关的海平面上升和极端天气迅速加剧了海岸侵蚀和洪水泛滥，并对经济产生了影响。联合王国政府的“韧性国家繁荣”成果认识到，需要实施适应气候、安全和可持续的沿海管理，重点是自然过程。可持续适应的规划和实施需要新的、有效的工具来预测十年尺度的沿海变化。沿海行为固有的不确定性意味着这些行为必须建立在人工智能（AI）等先进技术的基础上，以便针对多种场景进行有效和可靠的测试。该项目将为应对这一挑战做出重大贡献，并通过采用顺序学习人工智能仿真框架来训练和验证一种有效的人工智能工具，以模拟时间依赖性，十年尺度的沿海形态动力学变化。一个经过充分验证的基于高保真过程的沿海计算模型，由多个十年尺度的连续随机时间序列的海况驱动，使用蒙特卡罗方法生成，将提供人工智能仿真器的训练和验证的合成海滩变化数据，并解决广泛接受的数据稀缺问题。模拟器将是一个强大的替代计算成本高的传统建模方法预测十年尺度的海滩变化在当地和全球范围内，并发现基于自然的，耐气候的沿海管理干预措施的工具。

Annual UK cost of flood and coastal erosion damage is estimated at £540 million. Sea level rise and extreme weathers associated with climate change rapidly increases coastal erosion and flooding, and economic impacts. The UK Government's Resilient Nation prosperity outcome recognises the need to implement climate-adaptive, safe, and sustainable coastal management, focusing on natural processes. Planning and implementation of sustainable adaptation requires new, efficient tools to predict decadal-scale coastal change. The uncertainty that is inherent in coastal behaviour means that these must be built on advanced technologies such as Artificial Intelligence (AI) to allow efficient and robust testing against multiple scenarios. This project will make a major contribution to tackle this challenge and train and validate an efficient AI tool to emulate time-dependent, decadal-scale coastal morphodynamic change by adopting a sequential learning AI emulation framework. A well-validated high-fidelity process-based coastal computational model, driven by multiple decadal-scale continuous stochastic time series of sea states, generated using a Monte Carlo approach, will provide synthetic beach change data for training and validation of the AI emulator and address the widely accepted data-scarcity issue. The emulator will be a powerful surrogate to computationally costly conventional modelling approaches for predicting decadal-scale beach change at local and global scale, and a tool to discover nature-based, climate-resistant coastal management interventions.