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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利用近海波浪反射光谱的机器学习预测海滩剖面


跟踪和预测沿海形态的变化对于发展、减少风险和整体沿海管理至关重要。当前海岸研究和工程面临的一个挑战是找到一种能够准确评估海岸测深剖面以及坡度和沙洲等关键参数的方法。传统的水深测量是通过回声测深获得的，这是耗时、危险且昂贵的。使用各种模拟案例，我们测试了机器学习（特别是神经网络）根据岸基波浪反射从近海感测波浪重建海岸测深剖面的潜力。可以捕获前滩坡度、曲率、沙洲幅度和位置等特征。