Improving Significant Wave Height Prediction Using a Neuro-Fuzzy Approach and Marine Predators Algorithm

Adnan, Rana Muhammad; Cao, Xiaohan; Sadeghifar, Tayeb; Kuriqi, Alban; Kisi, Özgür; Shahid, Shamsuddin

doi:10.3390/jmse11061163

Cited by 14 publications

(3 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In comparison to the initial ECMWF SWH projections, the spring correction proved to be the most successful, with a mean absolute error reduction of 12.972-46.237%. Ikram and Cao [24] investigated the performance of a unique hybrid neuro-fuzzy model for the short-term (one hour to one day) prediction of SWH. This model combines the Adaptive-Network-based Fuzzy Inference System (ANFIS) method with the Marine Predator Algorithm (MPA), improving the accuracy of ANFIS-PSO and ANFIS-GA by 8.30% and 11.20%, respectively, in terms of root mean square errors during the prediction of a 1 h lead time in the test period.…”

Section: Introductionmentioning

confidence: 99%

Significant Wave Height Forecasting Based on EMD-TimesNet Networks

Ouyang,

Gao,

Zhang

et al. 2024

JMSE

View full text Add to dashboard Cite

Significant Wave Height (SWH) is a crucial parameter in ocean wave dynamics, impacting coastal safety, maritime transportation, and meteorological research. Building upon the TimesNet neural network, a recent advancement in the realm of time series prediction in deep learning, this study proposes an integrated approach combining Empirical Mode Decomposition (EMD) with TimesNet, introducing the EMD-TimesNet model for SWH forecasting. The TimesNet model’s multidimensional spatial mapping guarantees effective historical information extraction, while the EMD approach makes it easier to decompose subsequence characteristics inside the original SWH data. The predicted Root Mean Square Error (RMSE) and Correlation Coefficient (CC) values of the EMD-TimesNet model are 0.0494 m and 0.9936; 0.0982 m and 0.9747; and 0.1573 m and 0.9352 at 1 h, 3 h, and 6 h, respectively. The results indicate that the EMD-TimesNet model outperforms existing models, including the TimesNet, Autoformer, Transformer, and CNN-BiLSTM-Attention models, both in terms of overall evaluation metrics and prediction performance for diverse sea states. This integrated model represents a promising advancement in enhancing the accuracy of SWH predictions.

show abstract

Section: Introductionmentioning

confidence: 99%

Significant Wave Height Forecasting Based on EMD-TimesNet Networks

Ouyang,

Gao,

Zhang

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

“…In a relevant study, Pourzangbar et al [13] demonstrated that the prediction of scour depth due to non-breaking waves with the aid of Machine Learning (ML) models such as regression trees and support vector regression achieves the highest accuracy. Therefore, soft computing and ML methods, which fall under artificial intelligence methodologies, have been successfully utilized in modeling wave characteristics and wave heights [14][15][16]. Besides the beneficial features of the ML methodologies, it should be mentioned that the majority of ML models do not offer a simple, explicit description of the mathematical structure of the constructed models.…”

Section: Introductionmentioning

confidence: 99%

Modeling Significant Wave Heights for Multiple Time Horizons Using Metaheuristic Regression Methods

et al. 2023

Self Cite

View full text Add to dashboard Cite

The study examines the applicability of six metaheuristic regression techniques—M5 model tree (M5RT), multivariate adaptive regression spline (MARS), principal component regression (PCR), random forest (RF), partial least square regression (PLSR) and Gaussian process regression (GPR)—for predicting short-term significant wave heights from one hour to one day ahead. Hourly data from two stations, Townsville and Brisbane Buoys, Queensland, Australia, and historical values were used as model inputs for the predictions. The methods were assessed based on root mean square error, mean absolute error, determination coefficient and new graphical inspection methods (e.g., Taylor and violin charts). On the basis of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) statistics, it was observed that GPR provided the best accuracy in predicting short-term single-time-step and multi-time-step significant wave heights. On the basis of mean RMSE, GPR improved the accuracy of M5RT, MARS, PCR, RF and PLSR by 16.63, 8.03, 10.34, 3.25 and 7.78% (first station) and by 14.04, 8.35, 13.34, 3.87 and 8.30% (second station) for the test stage.

show abstract

“…The MPA-CNN included a combination of heavy feature extraction and classification techniques. Ikram et al [22] proposed a hybrid model with the ANFIS and MPA to estimate short-term significant wave heights. The results were compared with two different models, the ANFIS with the GA (ANFIS-GA) and the ANFIS with PSO (ANFIS-PSO).…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Method Based on the Marine Predators Algorithm and Adaptive Neuro-Fuzzy Inference System for the Identification of Nonlinear Systems

Baştemur Kaya

2023

Symmetry

View full text Add to dashboard Cite

In this study, a hybrid method based on the marine predators algorithm (MPA) and adaptive neuro-fuzzy inference system (ANFIS) is presented to identify nonlinear systems exhibiting symmetrical or asymmetrical behavior. In other words, the antecedent and conclusion parameters of the ANFIS are adjusted by the MPA. The performance of the MPA is evaluated on eight nonlinear systems. The mean squared error is used as the error metric. Successful results were obtained on the eight systems. The best mean training error values belonging to the eight systems are 1.6 × 10−6, 3.2 × 10−3, 1.5 × 10−5, 9.2 × 10−6, 3.2 × 10−5, 2.3 × 10−3, 1.7 × 10−5, and 8.7 × 10−6. In the ANFIS training carried out to solve the related problems, the performance of the MPA was compared with the butterfly optimization algorithm, the flower pollination algorithm, moth–flame optimization, the multi-verse optimizer, the crystal structure algorithm, the dandelion optimizer, the RIME algorithm, and the salp swarm algorithm. The results have shown that the performance of the MPA mostly outperforms other algorithms in both training and testing processes.

show abstract

Improving Significant Wave Height Prediction Using a Neuro-Fuzzy Approach and Marine Predators Algorithm

Cited by 14 publications

References 57 publications

Significant Wave Height Forecasting Based on EMD-TimesNet Networks

Significant Wave Height Forecasting Based on EMD-TimesNet Networks

Modeling Significant Wave Heights for Multiple Time Horizons Using Metaheuristic Regression Methods

A Novel Hybrid Method Based on the Marine Predators Algorithm and Adaptive Neuro-Fuzzy Inference System for the Identification of Nonlinear Systems

Contact Info

Product

Resources

About