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首页> 外文期刊>Journal of Hydroinformatics >ANFIS and ANN models for the estimation of wind and wave-induced current velocities at Joeutsu-Ogata coast
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ANFIS and ANN models for the estimation of wind and wave-induced current velocities at Joeutsu-Ogata coast

机译：ANFIS和ANN模型用于估计上越绪方海岸风和波浪感应的流速

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摘要

In this study, the adaptive network-based fuzzy inference system (ANFIS) and artificial neural network (ANN) were employed to estimate the wind-and wave-induced coastal current velocities. The collected data at the Joeutsu-Ogata coast of the Japan Sea were used to develop the models. In the models, significant wave height, wave period, wind direction, water depth, incident wave angle, and wind speed were considered as the input variables; and longshore and cross-shore current velocities as the output variables. The comparison of the models showed that the ANN model outperforms the ANFIS model. In addition, evaluation of the models versus the multiple linear regression and multiple nonlinear regression with power functions models indicated their acceptable accuracy. A sensitivity test proved the stronger effects of wind speed and wind direction on longshore current velocities. In addition, this test showed great effects of significant wave height on cross-shore currents' velocities. It was concluded that the angle of incident wave, water depth, and significant wave period had weaker influences on the velocity of coastal currents.
机译：在这项研究中，基于自适应网络的模糊推理系统（ANFIS）和人工神经网络（ANN）被用来估计风浪引起的沿海电流速度。使用日本海的上越绪方海岸的收集数据来开发模型。在模型中，有效波高，波浪周期，风向，水深，入射波角和风速被视为输入变量。以及沿海和跨岸流速作为输出变量。模型的比较表明，ANN模型优于ANFIS模型。此外，与幂函数模型的多元线性回归和多元非线性回归相比，模型的评估表明其可接受的准确性。敏感性测试证明，风速和风向对近岸海流速度的影响更大。此外，该测试还显示出显着的波高对跨岸海流速度的巨大影响。得出的结论是，入射波的角度，水深和重要的波周期对沿海水流速度的影响较小。

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