Mohammad Zeynoddin; Hossein Bonakdari
Abstract
Given the climate changes, achieving rainfall forecast is of high importance and facing such challenges affected markedly in vast areas of societies. Accordingly, numerous nonlinear and linear methods have been developed. Most hydrological phenomena like rainfall are consisted of both linear and nonlinear ...
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Given the climate changes, achieving rainfall forecast is of high importance and facing such challenges affected markedly in vast areas of societies. Accordingly, numerous nonlinear and linear methods have been developed. Most hydrological phenomena like rainfall are consisted of both linear and nonlinear parts. Modeling such phenomenon with stochastic methods like seasonal auto regressive moving average model (SARIMA), which are linear, demands data preparation prior to modeling. In this study, by investigating different forms of data preparation methods, variations in stochastic modeling results are scrutinized. The pre-processing methods used are categorized in two parts, normalization and stationarzition of data. The rainfall series is initially normalized by 4 transforms, namely: Manly(Mn), John-Draper (JD), Yeo-Johnson (YJ) and Scaling (Sc). The series, then, are stationarized by differencing, standardization (Std) and spectral analysis (Sf). After achieving preferred results by numerous tests, the preprocessed data are then modeled by stochastic SARIMA model. With regards to error and model sufficiency indices and graphs results, the acceptable results, but not the best, was obtained by the Sc-Diff combination, with SARIMA (0,0,1) (3,0,3)12 model and coefficient of determination, 0.355, variance accounted for, 0.353, root mean square error, 0.313, scatter index, 1.030, mean absolute error, 21.355), corrected Akaike Information Criterion, 1227.03. The results revealed that concerning the severe fluctuations in data, a supplementary method, like hybridization with artificial intelligence (AI) methods, is needed to achieve preferable results.
Azadeh Gholami; Hossein Bonakdari; Ali Akbar Akhtari
Volume 3, Issue 1 , June 2016, , Pages 193-200
Hossein Bonakdari; Gislain Lipeme-Kouyi; Girdhari Lal Asawa
Volume 1, Issue 2 , June 2014, , Pages 51-56
Abstract
The developing turbulent flow in an open channel is a complex three-dimensional flow influenced by the secondary currents and free surface effects and is, therefore, not amenable to analytical solution. This paper aims to study the impact of three key hydraulic parameters (relative roughness, the Froude ...
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The developing turbulent flow in an open channel is a complex three-dimensional flow influenced by the secondary currents and free surface effects and is, therefore, not amenable to analytical solution. This paper aims to study the impact of three key hydraulic parameters (relative roughness, the Froude number and the Reynolds number) on the establishment length using computational fluid dynamic (CFD) analysis. CFD analysis is based on the use of the ANSYS-CFX commercial code. The CFD strategy of modelling is validated against experimental velocity distribution in a cross-section and a good agreement is achieved. A dimensionless length is suggested for predicting the length of the developing flow zone for rectangular open channel. A linear relationship has also been developed for assessing the establishment length.
