Container Terminal Demand Forecasting Framework Using Fuzzy-GMDH and Neural Network Method

Hwang, Hark; Bae, Suk-Tae; Cho, Gyu-Sung

doi:10.1109/icicic.2007.225

Cited by 5 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As also described in Table 4, the MAPE values indicate that the GMDH-NN model gives the minimum error when compared to the other two models, all through the data series for prediction of n. The MAPE values are observed to be less than 7% for GMDH-NN model, whereas the MAPE values of MARS and SVR models greater than 9% in testing phase throughout the range of data. GMDH has been used for the identification of a mathematical model that has many input variables, but limited data needs by using a hierarchical structure [70]. Though we have not studied the computation time of these models used in this study, however various authors reported that SVR models required more time to train [71,72].…”

Section: Comparison Of Predictive Modelsmentioning

confidence: 99%

Anticipate Manning’s Coefficient in Meandering Compound Channels

2018

View full text Add to dashboard Cite

Estimating Manning's roughness coefficient (n) is one of the essential factors in predicting the discharge in a stream. Present research work is focused on prediction of Manning's n in meandering compound channels by using the Group Method of Data Handling Neural Network (GMDH-NN) approach. The width ratio (α), relative depth (β), sinuosity (s), Channel bed slope (S o ), and meander belt width ratio (ω) are specified as input parameters for the development of the model. The performance of GMDH-NN is evaluated with two different machine learning techniques, namely the support vector regression (SVR) and multivariate adaptive regression spline (MARS) with various statistical measures. Results indicate that the proposed GMDH-NN model predicts the Manning's n satisfactorily as compared to the MARS and SVR model. This GMDH-NN approach can be useful for practical implementation as the prediction of Manning's coefficient and subsequently discharge through Manning's equation in the compound meandering channels are found to be quite adequate.Hydrology 2018, 5, 47 2 of 21 hydraulic parameters. Formulation for Manning's roughness showing the gradient effect in channels having longitudinal slopes greater than 0.002 was derived by Jarrett [7]. Due to variation in flow depth, geometry and slope, the distinction of roughness coefficient in a straight channel as compared to a meandering channel were discussed by Arcement and Schneider [8]. Yen [9] proposed Manning's n for simple uniform flows taking account for a geometric measure, such as unevenness of the edge. Further, SCS method was linearized by James [10] for a meandering channel, named the Linearized SCS (LSCS) and recommended the Manning's roughness value for the different sinuosity channels. Shiono et al. [11] developed a model to predict discharge considering the Manning's n in the case of longitudinal slope and the meander effect of the channel. Jena [12] proposed an equation to calculate the n by considering the effect of channel width, longitudinal channel slope, and the flow depth of the channel. The variations of wetted area, wetted perimeter, and the velocity data on Manning's roughness coefficient by considering the flow simulation and sediment transport in irrigated channels was investigated by Mailapalli et al. [13]. Khatua et al. [14-16] formulated a mathematical equation for roughness coefficients by varying the sinuosity and geometry of the meandering compound channel. Xia et al. [17] and Barati [18-20] carried out the experiments for predicting discharge by taking care of the effect of bed roughness. Dash et al. [21] modeled the Manning's roughness coefficient by considering the aspect ratio, viscosity, slope of the bed, and sinuosity. Pradhan et al. [22] proposed an empirical formulation of predicting Manning's n by dimensional analysis for the compound meandering channel, which affects relative depth, width ratio, longitudinal channel slope, and sinuosity.In the recent years, ML techniques have been successfully implemented for predicting complex phenom...

show abstract

Section: Comparison Of Predictive Modelsmentioning

confidence: 99%