Explicit wave models and expensive sensor equipment capable of predicting and measuring wave parameters often carry a prohibitive computational and financial expense. To counter this, this paper proposes an alternative method for nowcasting coastal zone significant wave heights through the joint use of meteorological and structural data in the training of supervised machine learning models. In testing the hypothesis that structural data can improve model classification, artificial neural network and decision tree models were developed, trained and tested on field data recorded on a coastal jetty located in the southern coasts of Cyprus. A comprehensive investigation of the different models yields that the joint use of meteorological and structural features can improve classification performance, regardless of the network choice. It is also demonstrated that redundancy of training parameters could inject unwanted overfitting, reducing model generalization. To address this, a method for quantifying feature importance has been proposed by exploiting the nature of decision tree algorithms and the Gini impurity index, reaffirming that structural features do indeed benefit model classification. These results highlight the potential of tapping into the untapped pool of structural data for significant wave height prediction, paving the way for new research to be undertaken in this direction.

能够预测和测量波浪参数的显式波浪模型和昂贵的传感器设备通常会带来过高的计算和财务费用。为了解决这个问题，本文提出了一种在监督式机器学习模型的训练中通过联合使用气象和结构数据来对沿海地区显着海浪高度进行临近预报的替代方法。在检验结构数据可以改善模型分类的假设时，开发了人工神经网络和决策树模型，并对位于塞浦路斯南部海岸的沿海码头记录的现场数据进行了训练和测试。对不同模型的全面研究表明，无论使用哪种网络，结合使用气象和结构特征都可以提高分类性能。还证明了训练参数的冗余可能会注入不必要的过拟合，从而降低模型的泛化性。为了解决这个问题，通过利用决策树算法和基尼杂质指数的性质，提出了一种量化特征重要性的方法，重申了结构特征确实有利于模型分类。这些结果凸显了挖掘尚未开发的结构数据池进行重大波高预测的潜力，为在这一方向上开展新的研究铺平了道路。重申结构特征确实有利于模型分类。这些结果凸显了挖掘尚未开发的结构数据池进行重大波高预测的潜力，为在这一方向上开展新的研究铺平了道路。重申结构特征确实有利于模型分类。这些结果凸显了挖掘尚未开发的结构数据池进行重大波高预测的潜力，为在这一方向上开展新的研究铺平了道路。