An alternative approach for the prediction of significant wave heights based on classification and regression trees

Mahjoobi, J.; Etemad‐Shahidi, Amir

doi:10.1016/j.apor.2008.11.001

Cited by 121 publications

(48 citation statements)

References 13 publications

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“…For short-term modelling of wave parameters, different approaches of artificial neural networks (see e.g. Deo et al 2001;Mandal and Prabaharan 2006;Arena and Puca 2004;Makarynskyy et al 2005) and data mining techniques (Mahjoobi and Etemad-Shahidi 2008;Mahjoobi and Mosabbeb 2009) have successfully been applied. A non-linear threshold autoregressive model for the significant waveheight was proposed in Scotto and Guedes Soares (2000).…”

Section: Stationary Modelsmentioning

confidence: 99%

Long-term time-dependent stochastic modelling of extreme waves

Vanem

2010

Stoch Environ Res Risk Assess

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This paper presents a literature survey on timedependent statistical modelling of extreme waves and sea states. The focus is twofold: on statistical modelling of extreme waves and space-and time-dependent statistical modelling. The first part will consist of a literature review of statistical modelling of extreme waves and wave parameters, most notably on the modelling of extreme significant wave height. The second part will focus on statistical modelling of time-and space-dependent variables in a more general sense, and will focus on the methodology and models used also in other relevant application areas. It was found that limited effort has been put on developing statistical models for waves incorporating spatial and long-term temporal variability and it is suggested that model improvements could be achieved by adopting approaches from other application areas. In particular, Bayesian hierarchical space-time models were identified as promising tools for spatio-temporal modelling of extreme waves. Finally, a review of projections of future extreme wave climate is presented.

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Section: Stationary Modelsmentioning

confidence: 99%

Long-term time-dependent stochastic modelling of extreme waves

Vanem

2010

Stoch Environ Res Risk Assess

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“…If the dependent variable is categorical, the CART produces a classification tree. When the dependent variable is continuous, it produces a regression tree (Mahjoobi and Etemad-Shahidi, 2008). The evaluation function used for splitting in the CART is the impurity GINI index (Kearns and Mansour, 1999;Tan et al, 2006) …”

Section: Cart Classifiermentioning

confidence: 99%

Decision Tree–Based Classifier Combined with Neural-Based Predictor for Water-Stage Forecasts in a River Basin During Typhoons: A Case Study in Taiwan

Tsai

Wei

2012

Environmental Engineering Science

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To solve the complicated problem of water-stage predictions under the interaction of upstream flows and tidal effects during typhoon attacks, this article presents a novel approach to river-stage predictions. The proposed CART-ANN model combines both the decision trees (classification and regression trees [CART]) and the artificial neural network (ANN) techniques, which comprise the multilayer perceptron (MLP) and radial basis function (RBFNN). The combined CART-ANN model involves a two-step predicting process. First, the CART stage-level classifier can classify the river stages into higher, middle, and lower levels. Then, the ANN-based water-stage predictors are employed to predict the water stages. The proposed model was applied to the Tanshui River Basin in Taiwan. The Taipei Bridge, which is close to the estuary and affected by tidal effects, was taken as the study gauge. The mean square error and the mean absolute error were used for evaluating the variance and bias performances of the models. This study makes two contributions. First, the CART-MLP and CART-RBF were modeled to predict river stages under tidal effects during typhoons, and they were compared with three benchmark models, CART, back-propagation neural network, and RBFNN. Second, the CART-RBF successfully demonstrated that it achieved more accurate prediction than CART-MLP and three benchmark models.

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“…At the end, the smoothing procedure is used to improve the predictions. More details are given by Breiman et al [29] and Mahjoobi and Etemad-Shahidi [30]. This method has been used successfully in engineering and environmental science, e.g., Refs.…”

Section: Classification and Regression Trees (Cart) Algorithmmentioning

confidence: 98%

“…[25]) or turbulent flow theory (e.g., Ref. [30]). In most of these studies, fluid and sediments are not considered as two phases and simplified assumptions are made to estimate the sediment transport and depth of the scour hole [27].…”

Section: Vsmentioning

confidence: 99%

Classification and Regression Trees Approach for Predicting Current-Induced Scour Depth Under Pipelines

Yasa

Etemad‐Shahidi

2013

Journal of Offshore Mechanics and Arctic Engineering

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Reliable prediction of scour depth is important in engineering analysis concerned with pipeline stability. The aim of this study is to develop an accurate formula for prediction of the current-induced scour depth under pipelines. Previous experimental data are collected and used as a database by which to study the effect of different parameters on the scour depth. Decision tree and nonlinear regression approaches are used to develop engineering design formulae for estimation of the current induced scour depth in both live bed and clear water conditions. It is demonstrated that the proposed formulas are more accurate than previous ones in predicting the scour depth in all conditions. Probabilistic formulas are also presented for different levels of risk, aimed at safe and economic design of submerged pipelines.

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An alternative approach for the prediction of significant wave heights based on classification and regression trees

Cited by 121 publications

References 13 publications

Long-term time-dependent stochastic modelling of extreme waves

Long-term time-dependent stochastic modelling of extreme waves

Decision Tree–Based Classifier Combined with Neural-Based Predictor for Water-Stage Forecasts in a River Basin During Typhoons: A Case Study in Taiwan

Classification and Regression Trees Approach for Predicting Current-Induced Scour Depth Under Pipelines

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