Tracking and forecasting changes in coastal morphology is vital for development, risk reduction, and overall coastal management. One challenge of current coastal research and engineering is to find a method able to accurately assess the bathymetry profile along the coast and key parameters such as slope and sandbars. Traditional bathymetry measurements are obtained through echo-sounding, which is time-consuming, hazardous and costly. Using a variety of simulated cases, we test the potential of machine learning and in particular Neural Networks to reconstruct the coastal bathymetry profile from offshore sensed waves, based on shore-based wave reflection. Features such as foreshore slope, curvature, sandbars amplitude and positions can be captured.

中文翻译：

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通过机器学习从海上波浪反射光谱中预测海滩剖面


跟踪和预测沿海地貌的变化对于开发、降低风险和整体沿海管理至关重要。当前沿海研究和工程的一个挑战是找到一种能够准确评估沿海测深剖面和关键参数（如坡度和沙洲）的方法。传统的测深测量是通过回声探测获得的，这既耗时、危险又昂贵。使用各种模拟案例，我们测试了机器学习的潜力，特别是神经网络，以基于岸基波浪反射从海上感应波浪中重建沿海测深剖面。可以捕获前滩坡度、曲率、沙洲幅度和位置等特征。