Saeed Farzin; Hamid Mirhashemi; Hamed Abbasi; Zohreh Maryanaji; Payam Khosravinia
Abstract
In this study, long-term memory and dynamic behavior of daily flow time-series of Khorramabad River, which its basin is mountainous and has urban land use, is investigated by Hurst exponent. The Hurst exponent of runoff signal of Khorramabad River during 1991-2014 period was obtained as 0.8. This value ...
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In this study, long-term memory and dynamic behavior of daily flow time-series of Khorramabad River, which its basin is mountainous and has urban land use, is investigated by Hurst exponent. The Hurst exponent of runoff signal of Khorramabad River during 1991-2014 period was obtained as 0.8. This value shows long-term memory and nonlinear, dynamic signal of this river’s runoff. By applying neural network and wavelet transforms, the rainfall-runoff time-series of this river was simulated. In this respect, by taking the time-series of rainfall and rainfall-runoff as input to the artificial neural network and wavelet-neural network hybrid, four models including: 1) rainfall, neural network, 2) rainfall-runoff, neural network, 3) rainfall, wavelet-neural network and 4) rainfall-runoff, wavelet-neural network were developed. In the hybrid models of wavelet-neural network, time-series of rainfall and runoff were decomposed to high-frequency and low-frequency sub-signals. Results of evaluating the accuracy and efficiency of the four models showed that the wavelet–neural network model correctly simulated the runoff behavior with the best efficiency at 99% confidence level. Comparison of the results of wavelet–neural network model to the neural network model, using Morgan-Granger-Newbold, showed significant superiority of the first model. Also, results of evaluating signal error of the four implemented models, using two tests of Von-Neumann and Buishand test, showed that there is a significant substitution point in the signal error of the neural network model and signal of rainfall-runoff model. Therefore, existence of very different monthly and periodical fluctuations in 1991-1998 and 1999-2014 in the behavior of rainfall-runoff leads to reduction of efficiency and precision coefficient of neural network model. While, in the hybrid model of wavelet-neural network, allocation of relative weight to each sub-signal, has effectively reduced the short-term, average and long-term fluctuations in modeling error.
shahab nayyer; saeed farzin; hojat karami; mohammad rostami
Abstract
Erosion is one of the most worrisome issues associated with the river and coastal sides. The use of spur dikes is one of the newest methods for controlling and reducing erosion. The spur dikes are in various forms, such as simple, l-shaped and t-shaped. In this experimental study, the effect of different ...
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Erosion is one of the most worrisome issues associated with the river and coastal sides. The use of spur dikes is one of the newest methods for controlling and reducing erosion. The spur dikes are in various forms, such as simple, l-shaped and t-shaped. In this experimental study, the effect of different geometry of upstream and downstream spur dikes on the scouring of middle t-shaped spur dike was study for a series of spur dike combinations. Experiments have been analyzed for movable bed in the threshold of motion condition. The results of this study showed that the average scour depth around the mid-t-shaped spur dike is about 0.8 times the flow depth. The best performance of the t-shaped spur dike occurs when the upstream spur dike is l-shaped and downstream is t-shaped (L T T). In fact, the lowest volume and average scour depth due to the all situations is related to this combination. The erosion volume was calculated using the Surfer software. The average scouring volume of this combination is 0.063 m3 and the average scour depth is about 1.21 times the flow depth. At the site of the first spur dike, the entire amount of erosion on the side of spur dikes but by crossing the spur dikes, erosion is directed toward the opposite.
