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Prediction of significant wave height in Amir Abad Port using artificial intelligence methods (ANFIS, EANN, SVM) and evaluating its results with SWAN numerical model
Mohammadali Lotfollahi-yaghin * 1, Alireza Mojtahedi2 , Ata Aghayi3 , Elyaz Sadaghiani3
1- Professor, Faculty of Civil Engineering, Tabriz University , lotfollahi@tabrizu.ac.ir
2- Professor, Faculty of Civil Engineering, Tabriz University
3- Faculty of Civil Engineering, Tabriz University
Abstract:   (108 Views)
The significant wave height is a critical parameter in the design and analysis of marine structures, as well as in their operational use. Consequently, predicting this parameter greatly contributes to improving the design and analysis of marine structures. Various modeling approaches for wave characteristics include numerical, empirical, and artificial intelligence models. This study employs the SWAN model, which is a third-generation model for the simulation and estimation of wave characteristics. Furthermore, soft computing models, including individual and hybrid artificial intelligence models such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), and Emotional Artificial Neural Networks (EANN), have been utilized for wave height prediction, using data from the Amirabad buoy for validation purposes. In this research, the model inputs consist of wind speed, while the outputs are the wave heights. The analysis of the different models was carried out using statistical metrics, including bias, root mean square error, coefficient of variation, and coefficient of determination. The evaluation of the models using these statistics indicates an acceptable agreement between the significant wave heights estimated by the SWAN model and the buoy data. Additionally, each of the three artificial intelligence models mentioned demonstrates a relatively accurate capability in predicting wave height. A comparison of the results from the artificial intelligence models revealed that the Support Vector Machine model exhibited higher accuracy than the others. The Support Vector Machine model serves as an alternative method to the SWAN model or other numerical techniques, enhancing modeling outcomes when wave height data is unavailable or lacks the necessary statistical quality.
 
Keywords: Significant wave height, Amir abad port, Support Vector Machine, adaptive neuro-fuzzy inference system, artificial neuron networks, SWAN numerical model
     
Article Type: Original Research | Subject: Marine Structures
Received: 2024/11/24 | Accepted: 2025/03/11
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