Nasrin Beiranvand; Alireza Sepahvand; Ali Haghizadeh
Abstract
In this study, five soft computing techniques, GP-PUK, GP-RBF, M5P, REEP Tree and RF were used to predict the SL in Cham Anjir, Bahram Joo, Kaka Reza and Sarab Syed Ali hydrometry stations in Khorramabad, Biranshahr and Alashtar sub-watersheds, Lorestan province. Total data set consists of rain, discharge ...
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In this study, five soft computing techniques, GP-PUK, GP-RBF, M5P, REEP Tree and RF were used to predict the SL in Cham Anjir, Bahram Joo, Kaka Reza and Sarab Syed Ali hydrometry stations in Khorramabad, Biranshahr and Alashtar sub-watersheds, Lorestan province. Total data set consists of rain, discharge and solute load (SL) of three sub-watersheds out of which 70% data used to training and 30% data were used to testing phase. Finally, the models’ accuracy was assessed using three performance evaluation parameters, which were Correlation Coefficient (C.C.), Root Mean Square Error (RMSE) and Maximum Absolute Error (MAE). Results suggest that GP-PUK and GP-RBF models works well than other modeling approaches in estimating the SL in low and high water-periods. The result showed that, In the high-water period, in Cham Anjir, Sarab Said Ali and Kaka Reza stations the GP-RBF model and in the Bahram Joo station the GP-PUK model with the highest C.C and the lowest error were selected the optimal models in estimating the SL. Also, in the low water period, result shown that in Cham Anjir, Sarab Said Ali and Bahram Joo stations the GP-RBF model and in the Kaka Reza station the GP-PUK model were the best models in estimating the SL. Therefore, these models can be used to estimate the solute load of nearby rivers by/without hydrometry station for the management of the quantity and quality of surface water.
Bahram Mir Derikvand; Alireza Sepahvand; Hossein Zeinivand
Abstract
In recent years, extensive practices have been done on flood control, erosion and sediment in the fields of research and implementation of watershed management. Therefore, the purpose of this study was to assess the effects of watershed management practices on the characteristics of runoff and suspended ...
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In recent years, extensive practices have been done on flood control, erosion and sediment in the fields of research and implementation of watershed management. Therefore, the purpose of this study was to assess the effects of watershed management practices on the characteristics of runoff and suspended sediment load in two subwatersheds in Ghaleh Gol watershed in Lorestan province, Iran. In this research, for comparing the effect of watershed management practices (WMP) on discharge and suspended sediment load (SSL) from both subwatersheds, the flow velocity was measured and the SSL was sampled directly from the beginning of the rainfall events until the end of them. Results showed that in all measurements, the discharge and suspended sediment load of the southern subwatershed with watershed management practices was higher than the northern subwatershed without such practices. According to the results of ANOVA test, it was found that the difference between discharge peak (P=0.691) and suspended sediment load peak (P=0.840) was not significant in two subwatersheds. Also, according to the results, the difference between specific discharge and specific SSL was not significant (P>0.05). Based on these results, it was found that the implementation of WMP in the study area apparently has no the required performance to reduce the discharge and SSL, and the WMP have lost their performance before the end of their useful life. Therefore, in order to increase the performance of mechanical watershed management practices (MWMP), the biological and biomechanical practices has to be performed simultaneously.
Alireza Sepahvand; Hasan Ahmadi; Aliakbar Nazari Samani; Sebastiano Trevisani
Abstract
The geomorphometric indexes have been widely used for separation of surface landform features in the geomorphology science over the past decades. In this study, Multilayer Perceptron Neural Network (MPNN) was used to provide karstic landform classification. To that regard, initially, geomorphometric ...
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The geomorphometric indexes have been widely used for separation of surface landform features in the geomorphology science over the past decades. In this study, Multilayer Perceptron Neural Network (MPNN) was used to provide karstic landform classification. To that regard, initially, geomorphometric indicators were extracted from Digital Elevation Model (DEM), and then these indexes were used as neurons of input layer in artificial neural network. Furthermore, the box plots were applied to analyze the relationship between karstic landforms (such as dolines, hills, karstic plains, karstic valley and headland) and geomorphometric indexes. The results showed that 34, 6.9, 1.07, 48.5, 9.51 percent of the studying area are spatially covered by valleys, plains, dolines, highlands and hills respectively. It has also been found that the optimal structure of artificial neural networks for classification of landform is model No. 12-9-1 by having the learning rate 0.1 and 87.18 percent of determination coefficient. Also, it should be noted that the accuracy of the innovative method for classification of karstic landform is 90.58 percent. The analysis revealed that variations in geomorphometric indexes are very visible in the landform of hills, highlands and karstic valleys, whereas there are slightly overlapping in the plains and dolines.