TAYYEBEH MESBAHZADEH; ali azareh; Elham Rafiiei sardooi; Fateme FarzanePei
Abstract
Soil moisture, as the soil hydrologic parameters, can be affected by soil temperature and controls various hydrological processes. Given the importance of this issue, in this study, the efficiency of artificial neural network was studied to simulate soil temperature at 5- 100 cm depth. Recorded meteorological ...
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Soil moisture, as the soil hydrologic parameters, can be affected by soil temperature and controls various hydrological processes. Given the importance of this issue, in this study, the efficiency of artificial neural network was studied to simulate soil temperature at 5- 100 cm depth. Recorded meteorological parameters in the Isfahan synoptic station were used to simulate the soil temperature at different depths. The structure of the neural network was formed with an input layer, a hidden layer and an output layer and network training was done by Levenberg–Marquardt algorithm. Also test and error was done to determine a number of suitable neurons in hidden layer. The results showed that error in both neural network and ANFIS model increases with depth increase that can be due to the weak correlation between soil temperature changes in the lower layers and climatic parameters.
Mojtaba Nassaji zavareh; Bagher Ghermezcheshmeh; Fatemeh Rahimzadeh
Abstract
Daily constant discharges are needed estimating daily discharge in the hydrological model. The different number of statistical years, statistical deficiencies, and measurement error leads to the formation of time series with an uncommon time base. Hence the reconstruction of daily discharge data is of ...
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Daily constant discharges are needed estimating daily discharge in the hydrological model. The different number of statistical years, statistical deficiencies, and measurement error leads to the formation of time series with an uncommon time base. Hence the reconstruction of daily discharge data is of paramount importance. In this research, daily discharge was reconstructed in two stages in one of the upstream of Karoun River. In both stages of research, daily discharge data from two upstream stations were used to reconstruct daily discharge of the downstream station using artificial neural networks, neuro-fuzzy and two variables regression methods. In the second stage, the magnitudes of discharge, based on dry, normal and wet years was used to reconstruct the daily discharge. The results showed higher accuracy in the artificial neural network and neuro-fuzzy methods compared to two variable regression methods in the reconstruction of daily discharge. Multi-layer perceptron model has better potential among all different method of artificial neural network and neuro-fuzzy models. Classification of discharge into dry, normal, and wet years decreases error in the reconstruction of daily discharge. Based on the mean relative error (MRE), error in reconstruction of daily discharge is the least in normal, wet, and dry years, respectively
mohammad mirzavand; Hoda ghasemieh; mahmud akbari; seyed javad sadatinejad
Abstract
Kashan aquifer is adjacent to Salt Lake. Because of this adjacency, the saline water of the lake has moved to the aquifer. In this study groundwater quality of the aquifer was simulated using Artificial Neural Network (ANN) model. For this purpose, the dominant ion of water was first determined by Piper ...
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Kashan aquifer is adjacent to Salt Lake. Because of this adjacency, the saline water of the lake has moved to the aquifer. In this study groundwater quality of the aquifer was simulated using Artificial Neural Network (ANN) model. For this purpose, the dominant ion of water was first determined by Piper diagram. Results showed that the sodium chloride is the dominant ion of water and so it was selected as the target variable to be simulated So the output variable of the ANN model was the concentration of sodium chloride in current year while the input variables were the water table of groundwater, yearly rainfall and the concentration of sodium chloride in previous year. Result showed that Multilayer Perceptron ANN model has better result in predict of chlorine concentration compared to Radial Basis ANN model. The sensitivity analysis showed that concentration of chloride in previous year and water table of groundwater are the most important variables in the ANN model respectively.
Mahadi Vatakhah; Hamzeh Saidian
Abstract
Erosion and sediment movement phenomena are one of the most complex issues in management of rivers drainage areas that in water projects are very important. That its measurement wants high time and cost. Issue of surface runoff in river basin is a complex issue that human knowledge and understanding ...
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Erosion and sediment movement phenomena are one of the most complex issues in management of rivers drainage areas that in water projects are very important. That its measurement wants high time and cost. Issue of surface runoff in river basin is a complex issue that human knowledge and understanding about its physical laws a viewpoint of some mathematical formulas is limited. In this study to investigate modeling runoff and sediment production in different land uses of Aaghajari formation deposits, part of Margha watershed in Izeh city with area 1609 hectares was selected. In this study, some soil physical and chemical characteristics such as percentage of sand very fine, sand, clay, silt, pH, electrical conductivity, moisture, calcium carbonate and soil salinity in different land uses of Aghajari formation were used. Then the rain simulator in 7 point and with three replicated in different intensities 0.75, 1 and 1.25 mm in minute in three land use range, residential areas and agricultural lands, were used the amount of runoff and sediment. And the same of number were sampled in 0-20 cm in soil layer. In totally, 126 times sampling runoff and sediment were done. And 189 soil experiments were done. In order to perform all statistical analysis were used 11.5 SPSS and EXCEL and MATLAB 2008 software. The results showed that multi regression analysis in conditions with high input and little output data shows more favorable results than neural network. And in high intensities owing to data homogeny, neural network operation than to low precipitation intensities is better. But in multi regression in high and low precipitation intensities showed acceptable operation. The average of relative error in three land uses in sediment production in precipitation intensity 0.75 mm in minute were in multi regression 7.2 percent and root mean square error 0.06. And in neural network in same precipitation intensity the average of relative error 146/9 percent and root mean square error 0.41 were. The average of relative error in three land uses in sediment production in precipitation intensity 1 mm in minute were in multi regression 8.5 percent and root mean square error 0.19. And in neural network in same precipitation intensity the average of relative error 96.36 percent and root mean square error 0.85 were. The average of relative error in three land uses in sediment production in precipitation intensity 1.25 mm in minute were in multi regression 1.8 percent and root mean square error 0.38. And in neural network in same precipitation intensity were the average of relative error 37/6 percent and root mean square error 0.73.
Arash Malekian; Mahrou Dehbozorgi; Amir Houshang Ehsani; Amir Reza Keshtkar
Abstract
Consecutive droughts in Sistan and Baloochestan province cause water resources restriction and this isa very significant problem for this region. In this study, in order to forecast the drought cycle in 9climatological stations in the province, we used Artificial Neural Networks. The input data wereaverage ...
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Consecutive droughts in Sistan and Baloochestan province cause water resources restriction and this isa very significant problem for this region. In this study, in order to forecast the drought cycle in 9climatological stations in the province, we used Artificial Neural Networks. The input data wereaverage of annual rainfall data in all stations and also deciles precipitation index, which the first 30years from 1971 to 2000 used for training the network and the last 8 years from 2001 to 2008 forsimulating it. The network consists of Multilayer Perceptron (MLP) and Back Propagation Algorithm(BP) and also sigmoid transfer function. Number of Neurons in hidden layer was 10 with 1-10-1structure and was calculated based on the lowest RMSE. Then drought prediction was done in neuralnetwork with the trained algorithm and without using actual and observed data in 2009 to 2012.Results showed that, the network was able to simulate and forecast DPI index with 97% regressionand average RMSE error less than 5%. According to drought indices, results showed that the droughtwill have an increasing trend in all stations in this region in 2009 to 2011. Therefore, by using thismethod, drought can be predicted in later years without any need to have actual meteorological dataand also can be used in water resources management, drought management and climate changes.
Hamidreza Moradi; Alireza Sepahvand; Parviz Abdolmaleki
Abstract
More than 30% of Iran's land is formed from mountainous areas. So each year, landslides cause damages to structures, residential areas and forests, creating sedimentation, muddy floods and finally deposit the sediments in reservoir dams. Therefore, for preventing of this damages and expressing the sensitivity ...
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More than 30% of Iran's land is formed from mountainous areas. So each year, landslides cause damages to structures, residential areas and forests, creating sedimentation, muddy floods and finally deposit the sediments in reservoir dams. Therefore, for preventing of this damages and expressing the sensitivity rate of hillslopes, landslide hazard zonation is considered in prone areas. The purpose of this study is to determine the optimal structure of artificial neural network with different numbers of input factors for the landslide hazard zonation in the Haraz Watershed. First, the number of optimal epochs was determined to prevent network overlearning with trial and error method. Then, 14 neurons were determined in the hidden layer. Finally, the number of neurons was changed from 1 to 9 in the input layer. According to the obtained results, with increasing the number of neurons in the input layer, efficiency of Artificial Neural Network improved for landslide susceptibility mapping. In this research, nine neurons in the input layer, 14 neurons in the hidden layer and one neuron in the output layer were selected as the optimal structure. Root Mean Square Error and Descriptive Coefficient (R2) were equal to 0.051 and 0.962, respectively and the accuracy of landslide hazard zonation map was equal to 92.3%. Meanwhile, the results showed that about 35.14, 26.73, 14.59, 9.88, and 13.63 percent of all studied areas are located in stable, low, moderate, high and extremely hazardous areas, respectively.
A. Salajegheh; A. Fathabadi
Volume 62, Issue 2 , October 2009, , Pages 271-282
Abstract
Correct estimation of suspended sediment transported by a river is an important practice in water structure design, environmental problems and water quality issues. Conventionally, sediment rating curve used for suspended sediment estimation in rivers. In this method discharge and sediment discharge ...
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Correct estimation of suspended sediment transported by a river is an important practice in water structure design, environmental problems and water quality issues. Conventionally, sediment rating curve used for suspended sediment estimation in rivers. In this method discharge and sediment discharge or concentration related using regression relation that generally is exponential model. Respect to uncertainty and nonlinear relation between discharge and sediment concentration, sediment rating curve has not enough efficiency for this purpose. In this study using Artificial Intelligent (Fuzzy Logic and Artificial Neural Network), suspended sediment in Karaj River was estimated. First, various neural network and fuzzy logic models established. For neural network and fuzzy logic, models with four neuron in hidden layers and FIS (Fuzzy Inference System) with four Gaussian membership functions, respectively were selected as the best structure. Finally, the results showed that fuzzy logic estimates the suspended sediment loud better than the other techniques and therefore is suggested for estimation of suspended sediment load.
