Irrigation
F. Hayatgheibi; N. Shahnoushi; B. Ghahreman; H. Samadi; M. Ghorbani; Mahmood Sabouhi
Abstract
Introduction: The development of water resources in many cases has led to increased economic welfare, improved living and health standards, food production, etc. However, in some cases due to the insufficient attention to all aspects of these projects, the irreparable environmental effects and subsequent ...
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Introduction: The development of water resources in many cases has led to increased economic welfare, improved living and health standards, food production, etc. However, in some cases due to the insufficient attention to all aspects of these projects, the irreparable environmental effects and subsequent social and economic effects have been imposed on society. Paying attention to environmental water requirements is one of the most important issues in decision making in water resources development plans. The objective of this study is to assess river environmental water requirements in upstream and downstream of Beheshtabad Dam. Beheshtabad Dam has designed to build on the Karun River for water transfer from Karun to Zayanderood basin. But it has not been implemented due to the various problems and challenges. Materials and Methods: Protecting and restoring river flow regimes and hence, the ecosystems they support by providing environmental flows has become a major aspect of river basin management. Environmental flows describe the quantity, timing, and quality of water flows required to sustain freshwater,estuarine ecosystems,the human livelihoods, and well-being that depend on these ecosystems. Over 200 approaches for determining environmental flows now exist and used or proposed for use in more than 50 countries worldwide. In the present study, hydrological methods have been used. These methodes include Tennant and modified Tennant, Flow Duration Curve (FDC) and FDC shifting (for different environmental management classes). For this purpose, four hydrometric stations (three stations upstream and one station downstream of the dam) have been selected. Results and Discussion: The results of the study showed that the river water flow had not been sufficient to meet environmental water requirements in several cases, especially in years when the region was experiencing mild to moderate drought conditions. According to the Tennant method, the minimum environmental flow requirement averages based on Beheshtabad, DezakAbad, Kaj, and Armand stations data were 3.80, 5.06, 6.99, 22.01 m3/s, respectively. Using the mentioned stations data, , the minimum environmental flow requirement averages were 3.62, 6.07, 7.91, 23.67 m3/s based on the modified Tennant method. According to the flow duration curve method, minimum environmental flow requirements (Q95) were 1.96, 5.1, 8.32, 30.62 m3/s, using data collected from Beheshtabad, DezakAbad, Kaj, and Armand stations, respectively. The results of the flow duration curve shifting method indicated that the river water flow did not meet the river environmental water requirements in different environmental management classes in some months and years. Comparative results of different methods revealed that the minimum environmental flow requirement of Beheshtabad River upstream of Beheshtabad Dam was 1.22-16.75 m3/s from September to April (based on FDC shifting method, class C). The estimated minimum environmental flow for Koohrang River was 3.69-16.81 m3/s from September to April. The downstream of the dam, Karun River requires a minimum flow rate of 20.8-73.29 m3/s from September and October to April (based on FDC shifting method, class E). Conclusion: According to the results of various methods used in this study, the Karun River flow is not enough to meet the minimum river environmental water requirements in some years and months. Therefore, decision-makers must pay attention to the environmental water requirements in decisions related to the development plans and water transfer from this river. It should be noted that the river environmental water requirements have not been met completely when the region has experienced moderate or mild drought, which would be more acute in cases of more severe drought conditions. Therefore, the current surplus water of this basin may not be a sustainable source to transfer to another basin.
jalil javadi orte cheshme; mahmood kashefipoor
Abstract
Introduction: Nowadays, contamination of water is one of the problems that are more considered. Fecal Coliform (FC) is one of the most common indicator organisms for monitoring the quality of water. The problem that complicates the modeling of indicator organisms such as Fecal Coliform is determining ...
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Introduction: Nowadays, contamination of water is one of the problems that are more considered. Fecal Coliform (FC) is one of the most common indicator organisms for monitoring the quality of water. The problem that complicates the modeling of indicator organisms such as Fecal Coliform is determining the appropriate amount and an optimum rate of decay. It has been reported by many scientists that the decay coefficient or mortality rate is significantly affected by environmental elements. In this study, the effect of environmental parameters such as temperature, turbidity, radiation and suspended sediment concentration on the coliform decay coefficient hasbeen verified to have a dynamic and variable decay coefficient for better and reliable estimations of fecal coliform concentartion values.
Materials and Methods: Karun River is the longest and largest river in Iran. In this study, due to the accumulation of pollutants from industrial and agricultural wastes near Ahvaz city and for existence of quality measurement stations along the river, the Mollasani station to Farsiat station was selected to simulate and evaluate the hydrodynamic and quality of the river. The FASTER model has been used for modeling of the flow, sediment and water pollution. In this study, the dynamic roughness Manning coefficient has been used for more accurate simulate the flow, that had been added to the model by Mohammadi and Kashefipour. In Coliform bacteria and sediment modeling, some other dynamic parameters such as longitudinal dispersion coefficient are important and increasing or decreasing of these parameters are very significant and the accuracy of the Advection-Dispersion Equation (ADE) depends on the choice of the theoretical and/or experimental relations of these parameters. It was previously found that the Fisher equation performs the best for Karun river in modeling coliform, and this equation was therefore used in this study to calculate the dispersion coefficient. In order to investigate the effect of suspended sediment concentration on coliform decay rates, first this parameter must be modeled. In this research, the von Rijn method was used for modeling the suspended sediment load. In order to modeling the caliform, all dates of measuring were firstly determined in Zargan station; for each date the model was run for several times. For each run the decay coefficient was selected accordingly, until the predicted concentration by the model has the least difference inthe corresponding measured values. After that, the measured amount of environmental parameters such as Temperature, TUrbidity, RAdiation and also, the modeled values of suspended Sediment concentration wasdetermined for the same dates. Then, using a statistical software a relationship was developed to describe the decay coefficient as follows:
(1)
Results and Discussion: Using a statistical software, an equationfor decay coefficient was derived as follow:
(2)
Where K is decay coefficient (hr-1), T temperature (°C), TU turbidity (NTU), RA radiation(mmH2o-Vaporizeable) and Se suspended sedimentconcentration (kg/m3). Equation (2) was then added to the FASTER model, so the model was able to calculate the decay coefficient using the calculated suspended sediment at any time of simulation and this equation (dynamic decay coefficient). To be able to compare the dynamic decay coefficient and constant decay coefficient, the model was performed repeatedly for the whole calibration period and each time one constant K was given to the model. The best constant decay coefficient for the period of calibration and validation patterns was obtained to be K= 0.05 hr-1.Tables (1) and (2) show the amount of accuracy in predicting the suspended sediment concentration and coliform in both calibration and verification patterns, respectively. Table (1) shows that the FASTER model was able to estimate the suspended sediment concentration relatively accurate. Table (2) compares the effect of a constant decay coefficient versus the dynamic decay coefficient inaccurate estimation of fecal coliform concentrations.
Table 1- Comparison of the estimated error and correlation of suspended sediment
Pattern R2 a %E RMSE
Calibration 0.85 0.95 29.81 0.039
Verification 0.87 1.3 30.52 0.059
Table 2- Statistical parameters for coliform concentrations predicted and measured
Perioud k R2 a %E RMSE
Calibration Relation (2) 0.97 1.2 19 1906
0.05 0.92 2 50 4341
Verification Relation (2) 0.94 1.4 20 3860
0.05 0.77 1.5 44 7384
Conclusions: Comparison of the predicted fecal coliform concentrations with the corresponding measured values in the calibration and verification periodsshowed that the error estimate improved respectively about 31% and 24% when the dynamic decay coefficient was used instead of a constant value (the best constant value was obtained 0.05hr-1). The concentration of coliform bacteria in Zargan station during the total time of studying is more than 1000 CFU/100ml. Due to coliform bacteria concentrations and compared them with the levels allowed by the Standards, Karun river water is not suitable for human's drinking, confined livestock drink, food industry, oyster farming, irrigation products that are consumed raw and recreational uses (contact with water) like swimming.
Abazar Solgi; Amir Pourhaghi; Heidar Zarei; Hadi Ansari
Abstract
Introduction: Chemical pollution of surface water is one of the serious issues that threaten the quality of water. This would be more important when the surface waters used for human drinking supply. One of the key parameters used to measure water pollution is BOD. Because many variables affect the water ...
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Introduction: Chemical pollution of surface water is one of the serious issues that threaten the quality of water. This would be more important when the surface waters used for human drinking supply. One of the key parameters used to measure water pollution is BOD. Because many variables affect the water quality parameters and a complex nonlinear relationship between them is established conventional methods can not solve the problem of quality management of water resources. For years, the Artificial Intelligence methods were used for prediction of nonlinear time series and a good performance of them has been reported. Recently, the wavelet transform that is a signal processing method, has shown good performance in hydrological modeling and is widely used. Extensive research has been globally provided in use of Artificial Neural Network and Adaptive Neural Fuzzy Inference System models to forecast the BOD. But support vector machine has not yet been extensively studied. For this purpose, in this study the ability of support vector machine to predict the monthly BOD parameter based on the available data, temperature, river flow, DO and BOD was evaluated.
Materials and Methods: SVM was introduced in 1992 by Vapnik that was a Russian mathematician. This method has been built based on the statistical learning theory. In recent years the use of SVM, is highly taken into consideration. SVM was used in applications such as handwriting recognition, face recognition and has good results. Linear SVM is simplest type of SVM, consists of a hyperplane that dataset of positive and negative is separated with maximum distance. The suitable separator has maximum distance from every one of two dataset. So about this machine that its output groups label (here -1 to +1), the aim is to obtain the maximum distance between categories. This is interpreted to have a maximum margin. Wavelet transform is one of methods in the mathematical science that its main idea was given from Fourier transform that was introduced in the nineteenth-century. Overall, concept of wavelet transform for current theory was presented by Morlet and a team under the supervision of Alex Grossman at the Research Center for Theoretical Physics Marcel in France. After the parameters decomposition using wavelet analysis and using principal component analysis (PCA), the main components were determined. These components are then used as input to the support vector machine model to obtain a hybrid model of Wavelet-SVM (WSVM). For this study, a series of monthly of BOD in Karun River in Molasani station and auxiliary variables dissolved oxygen (DO), temperature and monthly river flow in a 13 years period (2002-2014) were used.
Results and Discussion: To run the SVM model, seven different combinations were evaluated. Combination 6 which was contained of 4 parameters including BOD, dissolved oxygen (DO), temperature and monthly river flow with a time lag have best performance. The best structure had RMSE equal to 0.0338 and the coefficient of determination equal to 0.84. For achieving the results of the WSVM, the wavelet transform and input parameters were decomposed to sub-signal, then this sub-signals were studied with Principal component analysis (PCA) method and important components were entered as inputs to SVM model to obtain the hybrid model WSVM. After numerous run this program in certain modes and compare them with each other, the results was obtained. One of the key points about the choice of the mother wavelet is the time series. So, the patterns of the mother wavelet functions that can better adapt to diagram curved of time series can do the mappings operation and therefore will have better results. In this study, according to different wavelet tests and according to the above note, four types of mother wavelet functions Haar, Db2, Db7 and Sym3 were selected.
Conclusions: Compare the results of the monthly modeling indicate that the use of wavelet transforms can increase the performance about 5%. Different structures and sensitivity analysis showed that the most important parameter which used in this study was parameter BOD, and then flow, DO and temperature were important. This means that the most effective BOD and temperature with minimum impact. Also between different kernels types, RBF kernel showed the best performance. So, combined wavelet with support vector machine is a new idea to predict BOD value in the Karun River.
R. Zamani; F. Ahmadi; F. Radmanesh
Abstract
Today, the daily flow forecasting of rivers is an important issue in hydrology and water resources and thus can be used the results of daily river flow modeling in water resources management, droughts and floods monitoring. In this study, due to the importance of this issue, using nonlinear time series ...
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Today, the daily flow forecasting of rivers is an important issue in hydrology and water resources and thus can be used the results of daily river flow modeling in water resources management, droughts and floods monitoring. In this study, due to the importance of this issue, using nonlinear time series models and artificial intelligence (Artificial Neural Network and Gen Expression Programming), the daily flow modeling has been at the time interval (1981-2012) in the Armand hydrometric station on the Karun River. Armand station upstream basin is one of the most basins in the North Karun basin and includes four sub basins (Vanak, Middle Karun, Beheshtabad and Kohrang).The results of this study shown that artificial intelligence models have superior than nonlinear time series in flow daily simulation in the Karun River. As well as, modeling and comparison of artificial intelligence models showed that the Gen Expression Programming have evaluation criteria better than artificial neural network.
N. Azam; M. Ghomeshi; Zh. Fayezizade; M. Mansouri Hafshejani
Abstract
The operation dredging of river bed and also creating a cut off for removing sharp meander and maintain proper alignment is effective non-structural methods for decreasing the flood level. This study compared the effects of 1) Dredging karoon river at ahvaz range, 2) Removing heterogeneity height in ...
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The operation dredging of river bed and also creating a cut off for removing sharp meander and maintain proper alignment is effective non-structural methods for decreasing the flood level. This study compared the effects of 1) Dredging karoon river at ahvaz range, 2) Removing heterogeneity height in order to make uniformly downstream slope, 3) Removing ahvaz downstream horseshoe Meander, which has been analyzed by HecRas4 model. Data modeling analysis showed that dredging in the form of bed dig in ahvaz range will not have significant effect on water level profile. This method only increase sedimentation and erosion rate in ahvaz range. Achieving to better results, the idea of dredging ahvaz downstream between two meander in order to regulate hydraulic gradient through removing heterogeneity height was proposed. Findings showed that this method has significant effect on improving of Hydraulic of flow and dredging effectiveness. Finally, it will be found that removing of the ahvaz downstream horseshoe meander is the most effective method for reducing level of flood and sedimentation rate in ahvaz range.
