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Khorram, Saeed and Mustafa Ergil, 2010. Most Influential Parameters for the Bed‐Load Sediment Flux Equations Used in Alluvial Rivers. Journal of the American Water Resources Association (JAWRA): 46(6):1065–1090. DOI: 10.1111/j.1752‐1688.2010.00468.x Abstract:  Problems of bed‐load sediment transport equations in alluvial rivers are addressed in this study where user‐friendly parameters were developed. To determine the influences of 300 parameters on the final result, 52 selected bed‐load equations for noncohesive particles (sand and gravel separately) were gathered and individually investigated. The influences of discrepancies among the computed and measured datasets were obtained by sensitivity analysis through multilinear regression method. The most influential parameters for the bed‐load sediment flux equations used to describe sand particles in alluvial rivers are: the gravitational power due to Shields’ parameter with an energy slope, the universal stream power due to critical Shields’ parameter with an energy slope, the Shields’ parameter ratio, the critical unit stream power, and the Shields’ parameter with energy slope. For gravel particles, the most influential parameters are: the universal stream power due to critical Shields’ parameter with an energy slope, the Shields’ parameter ratio, the gravitational power due to Shields’ parameter with an energy slope, the Shields’ parameter with an energy slope, and the Froude number of the channel. It is expected that researchers working in this field will be able to use these predicted parameters to generate new bed‐load sediment flux equations that give results that more closely agree with the actual values measured in alluvial rivers.

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Khorram, Saeed and Mustafa Ergil, 2010. A Sensitivity Analysis of Total‐Load Prediction Parameters in Standard Sediment Transport Equations. Journal of the American Water Resources Association (JAWRA) 46(6):1091–1115. DOI: 10.1111/j.1752‐1688.2010.00469.x Abstract:  The lack of a well‐defined, strong correlation between sediment transport load and the dominant variable selected for the development of a sediment transport equation is one of the fundamental reasons for discrepancies between computed and measured results under different flow and sediment conditions. Although several scholars have suggested different parameters, they unfortunately could not yet solve the problem. Twenty‐three total‐load equations for noncohesive particles were studied by providing insight into the relative strengths, weaknesses, and limitations. Three hundred parameters were investigated individually by using sensitivity analysis to pinpoint the key physical properties that control the errors. It is found that, the most influential parameter for the total‐load sediment flux equations used in alluvial rivers for the sand particles is the gravitational power due to Shields’ parameter with an energy slope. For the gravel particles, the most influential parameter is the universal stream power due to critical Shields’ parameter with an energy slope. Several graphs are presented to emphasize the effect of these parameters that were either directly used or were embedded within those equations. Recommendations and guides are also presented for the researchers working in this field.

Prediction of dissolved oxygen (DO) plays an important role in water resources especially in surface waters such as rivers. The oxygen affects a vast number of other water indicators. In this study, the artificial neural network (ANN) and a hybrid wavelet-ANN (WANN) models were considered to predict thirty minutes dissolved oxygen in the River Calder at the Methley Bridge Station was located in the UK. For the proposed WANN model, the discrete wavelet transform (DWT) was linked to the ANN model for DO prediction. To achieve this aim, the original time series of thirty minutes DO and temperature (T) were decomposed in several sub-time series by DWT, and these new sub-series were imposed to the ANN model. The results were compared with single ANN model. The comparisons were done by some of the widely used relevant physical statistic indices. The Nash-Sutcliffe coefficient values were 0.998 and 0.740 for the WANN and ANN models, respectively. The model computed values of DO by the WANN model were in close agreement with respective measured values in the river water. Elimination noise by DWT model during pre-processing data is one of the abilities of the WANN model to better prediction. Since the results indicate closer approximation of the peak DO values by the WANN model, this model could be used for the simulation of cumulative DO data prediction in thirty minutes ahead.