Mohammad Hossein Jahangir; Ahmad Nohegar; Keyvan Soltani
Volume 6, Issue 18 , June 2019, , Pages 39-56
Abstract
IntroductionThe impact of drought on different parts is not the same. In a situation where different regions of the country have experienced a significant decline in rainfall, its impact on water resources is still unclear or the decline of surface water resources has no effect on agricultural production ...
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IntroductionThe impact of drought on different parts is not the same. In a situation where different regions of the country have experienced a significant decline in rainfall, its impact on water resources is still unclear or the decline of surface water resources has no effect on agricultural production (Satari et al., 1395).Increasing or decreasing in hydrological time series can be described by changes in precipitation factors, evaporation, temperature, and the like (Nourani, 1395). Evaporation modeling from the reservoir level is important to predict the evaporation rate from the surface and the amount of water lost through evaporation and evacuated water and to have a proper planning to reduce the amount of this evaporation and its economic estimation. The heavy volume of computations and their time-consuming performance, especially in phenomena such as sudden floods, cause many financial losses and annoyances every year. One of these utilized and intelligent tools is artificial neural network which reaches acceptable output by establishing appropriate relationships between input variables in the shortest possible time and establishes the relationship with the output tool and provides the best possible result to experts. (Rajaei et al., 2010). In this regard, studies have been conducted in the world, including the study of the effect of different compounds of climatic parameters on the evaporation losses of the dam reservoir (Deswal & Pal, 2008).Methodology - Meteorological data routing nonlinearBefore proceeding to discuss the modeling and selecting the optimal model for the regions under discussion, the best nonlinear fittings are [1]obtained from the parameters affecting evaporation. For the study area, the fitting diagram for temperature data (oC), rainfall (mm), wind speed (Km/h), lake surface area (Km2) and evaporation (mm) were used, which resulted in the results and relationships for each of them.- Introducing Artificial Neural NetworkAn artificial neural network consists of three main layers of the input, the hidden (middle layer) and the output layers. The layer where the results of the model analysis are generated and the modeling is done is the output layer of the model (Fig. 1). The middle layer acts as the processor of the model and the processor nodes are at this stage (Traore et al., 2010). Fig.1 Artificial Neural Network structure with input, output and intermediate (hidden) layersResults- Artificial Neural Network Modeling for Minab DamIn order to use the artificial neural network, data from the Minab Dam was estimated from the data of the years 1998 to 2014 in MATLAB software. The best structures for the neural network are given in Table 1:Table.1 Error and correlation coefficient obtained by artificial neural networkNocorrelation coefficientMSE (Test)MSE (Learn)Neural network structure10.88490.0010.0016ANN(3,7,1)20.88490.000920.0014ANN(4,10,1)30.890.000880.0015ANN(4,11,1)- Training dataFor modeling of the neural network, 80% of the data was randomly selected by the MATLAB software. One of the most important diagrams used in neural network modeling is the actual values graph and evapotranspiration values using artificial neural network for training data (Fig. 2). Fig.2 Diagram of observation data and modeling at training stage, ANN [5,5,1]- Test dataThe remaining 20% of the data was also used to test the model obtained by the artificial neural network (Fig. 3). Fig.3 Diagram of observation data and modeling at testing stage, ANN [5,5,1] Discussion and conclusionEvaporation, as one of the natural parameters, has always been of interest to experts and researchers due to the high role that human has in reaching the outflow of water. In this research, we tried to evaluate the accuracy of this model by using the artificial neural network model in estimating evaporation from the lake level of the Minab Dam. In order to investigate the evolution of the evaporation parameters for the 19-year data, the best-fit nonlinear regression was drawn and the general trend of evolution of the effective parameters was studied. For modeling of the evaporation using artificial neural network, 19-year-old statistics between the years 1995 and 2013 were used.The best structure for estimating evaporation from the level of Minab Dam is selected in this paper. In this structure, the first and second layers have 5 neurons with 1000 replications to get the best result. The statistical coefficients obtained from the analysis using artificial neural network were considered in selecting the best structure. In this structure, the correlation coefficient with the value of 0.8941 had the highest value and the error values of training and testing the data were respectively 0.0011 and 0.0082. After this structures, ANN (3, 7,1), ANN (4,10,1), ANN (4,11,1), ANN (5,3,1) had acceptable correlation coefficient values and error in determining the amount of evaporation from the Minab dam.[1]- MSc Student, Faculty of New Sciences and Technologies, University of Tehran.
Abouzar Niknam; Ahmad Nohegar; Atefeh Jafarpoor; Mohammad Taghi Avand
Volume 5, Issue 16 , December 2018, , Pages 23-41
Abstract
Abstract
Introduction
Rivers are considered as one of the main sources of water and energy supply for humans, due to their special effects on human life and the formation of different civilizations. Therefore, their behavior should be considered. Flood flow is very complicated in natural rivers, especially ...
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Abstract
Introduction
Rivers are considered as one of the main sources of water and energy supply for humans, due to their special effects on human life and the formation of different civilizations. Therefore, their behavior should be considered. Flood flow is very complicated in natural rivers, especially in Meanderi Rivers. Therefore, the present research uses a numerical model to evaluate river floods characterized as different return periods of 10 and 50 years. The study area is located on the Kor River. Methodology
In this study, the geometry model and a numerical mesh system were calculated by taking advantage of topographic surveys and the required parameters for running CCHE2D were collected through filed works.
The CCHE2D model, a two-dimensional hydrodynamic model of flow and sediment transportation for unsteady flows, is able to simulate and analyze sediment transportation flows and morphological processes. It contains two parts:
1- CCHE-MESH generation for meshing the studied area
2- CCHE-GUI for applying the resulted mesh to simulate flow and sediment.
Finally, the model outputs including flow depth were obtained for the considered river reach in different return periods. Result
In this study, using the numerical model CCHE2D, changes in the depth of the water in two return periods of 10 and 50 years in different sections of the river route were obtained and the diagram of these changes was one of the outputs of the model.
Discussion and Conclusion
Study shows discharges with different return periods. In the Meander range of the study area, centrifugal force gradient flow of flow on the center and cross slope caused at the level of the water was so high, but the water level in the upper arch cross external and internal were decreasing. This phenomenon caused peripheral gradient pressure within the cross section which resulted in an imbalance of the local effect between the centrifugal force and gradient pressure flow, the secondary flow forms in the transverse section.
Once flooding occurs in the river (50-year return period), water level exceeds the main river channel and enters the surrounding floodplains. Under such circumstances, due to the differences between flood plains roughness and the main channel, flow rate (velocity) on flood plain is much slower than the main channel. Consequently, such difference leads to some shear layers in crossing points of the main channel and floodplain in the entrance, resulting in greater water turbulence. The comparison of the water flow velocity in different reach sections indicated that the highest water velocity was related to the first meander so that in return periods of 10 and 50 years it respectively reached 2.7 m/s and 3 m/s. The results of the study confirmed the applicability of the numerical model to predict river changes using flow parameters. Therefore, it can be said that the present numerical model is capable of analyzing the river changes in the wind tunnel channels in a desirable manner.
Ahmad Noheghar; Mohamad Kazemi; Seyyed Javad Ahamdi; Hamid Gholami; Rasool Mahdavi
Volume 4, Issue 10 , June 2017, , Pages 99-119
Abstract
Soil management is necessary in order to optimize utilization and decrease degradation. The present study aimed to measure the relative importance of the erosion rates and sediment yields of homogeneous units in land-uses and geological formations. Accordingly, Fargas, BLM models, and direct field measurements ...
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Soil management is necessary in order to optimize utilization and decrease degradation. The present study aimed to measure the relative importance of the erosion rates and sediment yields of homogeneous units in land-uses and geological formations. Accordingly, Fargas, BLM models, and direct field measurements of soil erosion were used. Then, the degree of homogeneous units' erosion on the map of land use and geology formation were extracted. In addition, the amount of the sediment caused by surface erosion, rill, and gully was measured. The total mean of sediments per land use and the geology information were measured. The areas including the participation of each of the produced sediments were also found. The results revealed that the highest amount of the sediment deposits in basin were for the range lands called B S33R42G21, C S34R43G32, and D S34R43G32 with the mean of 38.73(ton/ha) and for the Razak Information called C S43R42G21 and D s44R43G32 with the mean of 17.83(ton/ha). The highest amount of sediment deposits were also for the rangelands and Asmari formation, respectively, with the means of 64.9% and 55.43%. Bakhtyari formation and cultivation, in contrast, had the lowest relative importance in sediment yield of the Tange Bostanak watershed.
