Forecasting hurricane wave height in Gulf of Mexico using soft computing methods

Mafi, Somayeh; Amirinia, Gholamreza

doi:10.1016/j.oceaneng.2017.10.003

Cited by 34 publications

(13 citation statements)

References 47 publications

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“…In comparison to Kamranzad [5] and Somayeh [16], it is easy to see that the predictor selection process affects the performance of the prediction model, improving its accuracy. When using ANN for prediction, the results with input predictors selected by IF are more accuracy markedly.…”

Section: Resultsmentioning

confidence: 99%

Probabilistic Prediction of Significant Wave Height Using Dynamic Bayesian Network and Information Flow

Ming

Liu

2020

Water

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Short-term prediction of wave height is paramount in oceanic operation-related activities. Statistical models have advantages in short-term wave prediction as complex physical process is substantially simplified. However, previous statistical models have no consideration in selection of predictive variables and dealing with prediction uncertainty. This paper develops a machine learning model by combining the dynamic Bayesian network (DBN) with the information flow (IF) designated as DBN-IF. IF is focused on selecting the best predictive variables for DBN by causal analysis instead of correlation analysis. DBN for probabilistic prediction is constructed by structure learning and parameter learning with data mining. Based on causal theory, graph theory, and probability theory, the proposed DBN-IF model could deal with the uncertainty and shows great performance in significant wave height prediction compared with the artificial neural network (ANN), random forest (RF) and support vector machine (SVM) for all lead times. The interpretable DBN-IF is proven as a promising tool for nonlinear and uncertain wave height prediction.

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Section: Resultsmentioning

confidence: 99%

Probabilistic Prediction of Significant Wave Height Using Dynamic Bayesian Network and Information Flow

Ming

Liu

2020

Water

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“…The key objective of ANN training is to reduce the overall error E between the predicted and actual observations. The overall error of an ANN model, E, is mathematically expressed as [28]:…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…This kind of SVM uses quadratic programming techniques to find optimal hyperplanes that separate an input class from a target class. Quadratic programming can be expressed mathematically as [28]:…”

Section: Support Vector Machinementioning

confidence: 99%

Urban Water Demand Forecasting: A Comparative Evaluation of Conventional and Soft Computing Techniques

Oyebode

Ighravwe

2019

Resources

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Previous studies have shown that soft computing models are excellent predictive models for demand management problems. However, their applications in solving water demand forecasting problems have been scantily reported. In this study, feedforward artificial neural networks (ANNs) and a support vector machine (SVM) were used to forecast water consumption. Two ANN models were trained using different algorithms: differential evolution (DE) and conjugate gradient (CG). The performance of these soft computing models was investigated with real-world data sets from the City of Ekurhuleni, South Africa, and compared with conventionally used exponential smoothing (ES) and multiple linear regression (MLR). The results obtained showed that the ANN model that was trained with DE performed better than the CG-trained ANN and other predictive models (SVM, ES and MLR). This observation further demonstrates the robustness of evolutionary computation techniques amongst soft computing techniques.

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“…AI techniques can be used in solving engineering problems [4][5][6][7][8][9][10][11] even if the underlying relationships are unknown or the physical meaning is difficult to explain. This is one of the main advantages of these techniques when compared to most physically-based empirical and statistical methods.…”

Section: Overview Of Artificial Intelligencementioning

confidence: 99%

A review of Genetic Programming and Artificial Neural Network applications in pile foundations

Fatehnia

Amirinia

2018

Geo-Engineering

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Forecasting hurricane wave height in Gulf of Mexico using soft computing methods

Cited by 34 publications

References 47 publications

Probabilistic Prediction of Significant Wave Height Using Dynamic Bayesian Network and Information Flow

Probabilistic Prediction of Significant Wave Height Using Dynamic Bayesian Network and Information Flow

Urban Water Demand Forecasting: A Comparative Evaluation of Conventional and Soft Computing Techniques

A review of Genetic Programming and Artificial Neural Network applications in pile foundations

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