Maryam Asadi; Ali Fathzadeh
Abstract
Understanding of suspended sediment rate is one of the fundamental problems in water projects which water engineers consistently have involved with it. Wrong estimations in sediment transport cause incorrect design and destruction of hydraulic systems. Due to the difficulty of suspended sediment measurements, ...
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Understanding of suspended sediment rate is one of the fundamental problems in water projects which water engineers consistently have involved with it. Wrong estimations in sediment transport cause incorrect design and destruction of hydraulic systems. Due to the difficulty of suspended sediment measurements, sediment rating curves is considered as the most common method for estimating the suspended sediment load. The main purpose of this research is the capability challenge of this method in comparison to some state of the art models. In this study, we selected some computational intelligence models (i.e. K-nearest neighbor (KNN), artificial neural networks (ANN), Gaussian processes (GP), decision trees of M5, support vector machine (SVM) and evolutionary support vector machine (ESVM)) and compared them with their sediment rating model in 8 basins located in Gilan province. Daily sediment and discharge data considered as the input data for 30-years. Evaluation of the results indicated that the Gaussian process model has the lowest residual sum of squares (RMSE) and the highest correlation coefficient (r) than the other models.
Sharbanoo Abbasi Jondani; Ali Fathzadeh
Abstract
Snow is one of the main components of hydrological cycle in most of mountainous basins. Since collecting the snow data (e.g. snow water equivalent data) is very difficult and time consuming, some effort is necessary to develop methods to estimate spatially variation of snow depth distribution. In the ...
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Snow is one of the main components of hydrological cycle in most of mountainous basins. Since collecting the snow data (e.g. snow water equivalent data) is very difficult and time consuming, some effort is necessary to develop methods to estimate spatially variation of snow depth distribution. In the present study, the at-site SWE data of 14 stations located in the west of Isfahan providence for the period 1989-2010 were spatialized applying four methods composing the Kriging, the Co-Kriging, the Radial Basis Functions (RBF) and the Inverse Distance Weighting (IDW). In order to reach this purpose, first, the normality of data was checked using the Kolmogorov – Smirnov test. The homogeneity, the stability and the trend of data were tested employing the semivariogram approach. Then the appropriate data of each year was entered into the ArcGIS 9.3 to conduct the methods. Finally, the best method for spatializing the SWE data was selected based on the RMSE values. The results showed that the RBF method provided the best results for most of the years. Furthermore, it was found that the amount of SWE reduced from the south and west to the north and east of the basin.
Ali Fathzadeh; Somayeh Ebdam
Abstract
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[2] Agarwal, A., Mishra, S.K., Ram, S. and Singh, J.K. (2006). Simulation of runoff and sediment yield using artificial neural ...
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Mehdi Teimouri; Ali Fathzadeh
Abstract
The discharge data used for hydrological modeling should be the long-term suitable random data without trend and jump which is followed a specific statistical distribution. In this study, the above mentioned conditions were evaluated for 31 years period (1974-2004) of annual mean discharge data of 10 ...
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The discharge data used for hydrological modeling should be the long-term suitable random data without trend and jump which is followed a specific statistical distribution. In this study, the above mentioned conditions were evaluated for 31 years period (1974-2004) of annual mean discharge data of 10 gauging stations of West Azarbaijan province. For this purpose, the non-parametric Spearman correlation coefficient as well as Mann-Kendall method, non-parametric Run-test, non-parametric without distribution test of CUSUM and Kolmogorov–Smirnov test were used to trend, jump, stochastic and distribution analysis of data, respectively. The results showed that data of all stations were stochastic with no jump and trend (except Pol-e-Bahramloo gauging station). Also, data of most of the stations followed the gamma probability distribution function
