Ali Heidar Nasrolahi; yaser sabzevari
Abstract
In this research, in order to simulate the underground water level of Khorramabad plain, the performance of hybrid models of bat support vector regression, bat support vector regression, gray wolf support vector regression for four piezometric wells of Sarab Pardah, Naservand, Sally and Baba Hossein ...
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In this research, in order to simulate the underground water level of Khorramabad plain, the performance of hybrid models of bat support vector regression, bat support vector regression, gray wolf support vector regression for four piezometric wells of Sarab Pardah, Naservand, Sally and Baba Hossein Bridge, which have homogeneous statistics. and it was done without missing data. For modeling, rainfall (P), temperature (T) and underground water level (H) and withdrawal from water resources (q) have been used in the monthly report of the models during the period of 1380-1400. It should be noted that for modeling, 80% of the data is chosen for training and the remaining 20% for testing, randomly, which covers a wide range of data types. Correlation coefficient (R), root mean square error (RMSE), mean absolute value of error (MAE) and Sutcliffe coefficient of vitality (NS) were used to evaluate and compare the performance of the models. The results showed that the combined structure provides better performance than other structures in all the investigated models. Also, the results showed that the wavelet support vector regression model based on the evaluation indicators, in the piezometric well of Sarab Pardah has R=0.978, RMSE=0.221 m, MAE=0.011 m, NS=0.985 and also in the piezometric well of Naservand has R=0.981 . 010 m, NS=0.986 and finally piezometric well, Baba Hossein Bridge has 5 R=0.98, RMSE=0.101 m, MAE=0.007 m, NS=0.995, compared to other models, it can be used to create a favorable result.
Kaka Shahedi; mahtab forootan danesh
Abstract
Simulation of the rainfall-runoff process in a watershed is highly important from the point of view of hydrological issues, water resources management, river engineering, flood control structures and its storage. Rainfall-runoff estimation using a distributed hydrological model and the technique of GIS ...
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Simulation of the rainfall-runoff process in a watershed is highly important from the point of view of hydrological issues, water resources management, river engineering, flood control structures and its storage. Rainfall-runoff estimation using a distributed hydrological model and the technique of GIS has become possible, practical and common. The Wetspa model is a distributed model simulating runoff and water balance that is performed at different time scales including hourly or daily basis. In this research, the discharge is simulated using Wetspa model in the Ghorichay watershed. This watershed (as one of the sub-watersheds of Gorganrood) with an area of 2481.5 ha is located in the south of Ramian city in Golestan province. The data used by the model are land use maps, soil texture, digital elevation model, precipitation, evaporation, temperature and discharge (for calibration and validation of the model). Calibration of 13 parameters was performed manually and automatically for 7 years at the beginning of the statistical period and model validation was performed for a period of 5 years. The results of the model evaluation show the accuracy of discharge simulation and very good agreement between the simulated data and observations based on the Nash-Sutcliffe criterion of 67.21% in the calibration period and 76.34% in the validation period. Also, the discharge calculated by the Wetspa model for the whole watershed was 28.31%, which in comparison with the observed discharge of 30.12% indicates a good simulation of the model.
Geomorphology
hojatolah younesi; ahmad godarzi; Masoud Shakarami
Abstract
Today, hybrid models of artificial intelligence are considered as a suitable method for simulating hydrological phenomena, including quantitative estimation of river flow. For this purpose, there are various approaches in hydrology to estimate the flow rate of rivers, of which artificial intelligence ...
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Today, hybrid models of artificial intelligence are considered as a suitable method for simulating hydrological phenomena, including quantitative estimation of river flow. For this purpose, there are various approaches in hydrology to estimate the flow rate of rivers, of which artificial intelligence models are the most important. Therefore, in this study, the performance of support vector-wavelet regression, backup vector-gray wolf regression and bat-support vector regression models to simulate the flow of Kashkan river located in Lorestan province during the statistical period of 2010-2011 in the daily time scale were analyzed. The criteria of correlation coefficient, root mean square error and mean absolute value of error and bias were selected for evaluation and performance of the models. The results showed that the hybrid models have acceptable results in simulating the river discharge. Comparison of models also showed that the support-wavelet vector regression model in the validation stage showed values of R2 = 0.960, RMSE = 0.045, MAE = 0.024, NS = 0.968 and BIAS = 0.001 in predicting daily river flow. . Overall, the results showed that the use of hybrid support-wavelet regression model can be useful in predicting daily discharge.
Hydrogeomorphology
Erfan Bahrami; mehdi dastourani
Abstract
Estimation of flood hydrographs in the ungauged watersheds is a challenging issue in flood planning and management. Various models have been developed in this filed and it is necessary to evaluate the performance of models developed in different regions of the world with different climatic, hydrological ...
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Estimation of flood hydrographs in the ungauged watersheds is a challenging issue in flood planning and management. Various models have been developed in this filed and it is necessary to evaluate the performance of models developed in different regions of the world with different climatic, hydrological and physiographic features in order to comment on their performance in different regions. The Gamma synthetic unit hydrograph model is a developed model for estimating flood hydrographs in the ungauged watersheds with limited studies in the world. In this study, the Gamma synthetic unit hydrograph model for estimating flood hydrograph characteristics in Qareh-Sou watershed located in Kermanshah province in Iran has been investigated. Criteria for percentage error in peak discharge, percentage error in volume, mean absolute error, mean bias error, coefficient of determination and Kling-Gupta were estimated to evaluate the accuracy of simulation results. Based on the results, the mean values of the criteria expressed are 6.28, 17.4, 0.89, 0.54, 0.74 and 0.75, respectively, indicating that the Gamma synthetic unit hydrograph model is quite accurate in estimating the characteristics of the flood hydrograph in this study. In addition, the visual comparison of computational and observational hydrographs illustrates the remarkable accuracy of the Gamma synthetic unit hydrograph model in estimating the shape of flood hydrographs in the studied events.
Geomorphology
Saeid Roustami; Babak Shahinejad; Hojatolah Younesi; Hassan Torabipoudeh; Reza Dehghani
Abstract
Flood is one of the natural phenomena that causes a lot of human and financial losses in the world every year and creates many problems for the economic and social development of countries. Therefore, in order to reduce the damage, control and guidance of this phenomenon, estimating flood discharge and ...
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Flood is one of the natural phenomena that causes a lot of human and financial losses in the world every year and creates many problems for the economic and social development of countries. Therefore, in order to reduce the damage, control and guidance of this phenomenon, estimating flood discharge and identifying the factors affecting it is very important. In this study, in order to estimate the flood discharge of Kashkan catchment located in Lorestan province, new hybrid artificial intelligence models including artificial neural network - innovative gunner, artificial neural network - black widow spider and artificial neural network - chicken crowding during the period 1300-1400 were used. To evaluate the simulation performance, statistical indices of determination coefficient (R2), absolute mean error (MAE), Nash-Sutcliffe productivity coefficient (NSE), bias percentage (PBIAS) were used. The results showed that hybrid artificial intelligence models improve the performance of the single model. The results showed that the artificial neural network- innovative gunner model has more accuracy and less error than other models. Overall, the results showed that the use of hybrid artificial intelligence models is effective in estimating flood discharge and can be considered as a suitable and rapid solution in water resources management.
babak shahinejad; zohreh izadi; behzad javadi
Abstract
1-IntroductionRivers are the most important sources of drinking, agricultural, and industrial water supply. In recent decades, however, these resources have become the main receivers of sewer pipelines due to rapid population growth. To evaluate the effects of pollutant discharge on the self-purification ...
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1-IntroductionRivers are the most important sources of drinking, agricultural, and industrial water supply. In recent decades, however, these resources have become the main receivers of sewer pipelines due to rapid population growth. To evaluate the effects of pollutant discharge on the self-purification of rivers, it is necessary to use numerical simulations of water quality. Today, various softwares have been designed for this purpose. One of the most important of these softwares used in this research is the one-dimensional QUAL2Kw model that simulates water quality variables in a steady and non-uniform flow mode. In the present study, the water quality of the Khorramabad River was simulated with the help of this model over a distance of 35 km from the river.2-MethodologyThe range studied in this research is about 35 km along the Khorramabad River from the source of Khorramrud upstream of Robat Namaki village to Chamjangir hydrometric station, which in the geographical coordinates of 33°36'54" to 33°26'37" north latitude; it is located at 48°17'39" to 48°14'38" longitude. Khorramabad River pollution sources are divided into three main parts: urban, industrial, and agricultural. Due to the location of pollutant sources in the river, 5 points along the river were considered as sampling sites, two stations including the beginning and end of the study area, one station in the center of Khorramabad, and the other two stations were selected before the river entered the city and after leaving the city, respectively.In this research, the QUAL2Kw model version 5.1 was used. The required data of the model is divided into three parts: geometric-hydraulic data, qualitative data, and meteorological data. The river was divided into 11 sections and simulated using the hydraulic Manning equation. In this study, important water quality parameters such as DO, CBODf, COD, NO3, EC, and pH and temperature parameters in July and September of 2019 for calibration and validation, respectively, the model was used. Finally, RMSE, NRMSE, and MAE indices were used to evaluate the model in the simulation.3-Results and DiscussionThe results showed that the number of parameters including COD, CBODf, and NO3 increased after the Karganeh tributary joined the river and also the inflow of pollutant sources such as slaughterhouses, municipal treatment plant, milk factory, and alcohol production unit into the river. However, the pH (in both months) and EC (in July) parameters did not change much along the river; in other words, the river can self-purifying these parameters. In the research of Hashemi et al. (2019), for the simulation of the Talar River, the same result was obtained for these two parameters. Babakhani et al. (2019) in a study conducted on the Diwandara River reported a strong correlation between the measured and simulated values of the pH parameter because in surface water the pH value along the path with carbonate and bicarbonate in the path there reaches the equilibrium concentration. According to the results of the research and the fact that the Khorramabad River is used for agricultural and industrial purposes and is not a source of drinking water, at present, there is no limiting factor to achieve this purpose in the study route. Then, the calculation of statistical indices showed that the value of the NRMSE index in the calibration and validation stage of the model is the lowest for pH and equal to 8.83 and 9.22 percent and for EC is 11.05 and 13.86 percent, respectively. The simulation of DO parameter also had fluctuations along the river, while the statistical indices of NRMSE, RMSE, and MAE for this parameter in both calibration and validation stages were obtained at an acceptable level; thus, the above indices in the calibration stage of the model 12.49, 0.917 and 0.72, respectively, and in the validation stage of the model were calculated 24.65, 1.78, 1.55, respectively. In addition, the model was able to simulate the temperature parameter with high accuracy in July (RMSE = 1.92 and MAE = 1.57) and September (RMSE = 2.77 and MAE = 2.5709). Finally, the results of this study indicate the considerable accuracy of the QUAL2Kw model in simulating the above parameters in the Khorramabad River.4-ConclusionsThe results showed that the amount of chemical oxygen demand, biochemical oxygen demand, and nitrate parameters increased due to the entry of effluents from industrial pollutants. Besides, the evaluation index indicates that the QUAL2Kw model has shown good performance in estimating the acidity parameter compared to other parameters. It is suggested that in addition to the low water season, modeling be done in high water seasons and use two-dimensional quality models to simulate rivers. Keywords: Qualitative Parameters, Simulation, QUAL2Kw Model, Khorramabad River, Lorestan Province5-References Babakhani, Z., Saraee Tabrizi, M., & Babazadeh, H. (2019). Determining the Self-Purification capacity of Diwandara River using model qual2kw. Journal of Echo Hydrology, 6(3), 673-684.Hashemi, Z., Gholami Sefidkouhi, M. A., & Ahmadi, K. (2019). Evaluation and Simulation of Talar River Quality by using QUAL2KW Model. Iranian Journal of Irrigation & Drainage, 12(6), 1500-1510.
Hafez Mirzapour; Ali Haghizadeh; Rezvan Alijani; Zahedeh Heydarizadi
Volume 5, Issue 15 , October 2018, , Pages 153-169
Abstract
Abstract
Introduction
The importance of planning and managing water resources, as well as an increasing population growth and the limitation of surface water resources in the country, has made the accurate prediction of rivers' flow by using modern tools and methods of modeling, as an inevitable necessity. ...
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Abstract
Introduction
The importance of planning and managing water resources, as well as an increasing population growth and the limitation of surface water resources in the country, has made the accurate prediction of rivers' flow by using modern tools and methods of modeling, as an inevitable necessity. In addition, proper river flow prediction in river management, flood warning systems, and especially planning for optimal operation is required. In order to predict the flow of a river, several methods have been developed over the past years. In general, these methods can be classified into two categories of conceptual models and models based on statistics or data. The basis of the most of the predictive methods is the simulation type of the current status of the system which is called "modeling". Considering that in most cases the conceptual models require accurate data and knowledge of processes affecting the phenomenon, and this has so far been accompanied by many problems, researchers have turned to the statistical models. Over the past four decades, time-series models have been widely used in predictive river flow as a statistical model. In each science, the collected statistics corresponding to the variable which is going to be predicted and which is available in the past periods are called time series. Indeed, a time series is a set of statistical data collected at equal and regular intervals. Statistical methods that use such statistical data are called analytical methods of time series. The basis of many decisions in hydrological processes and decisions on exploitation of water resources is according to the prediction and analysis of time series. Assessment of the temporal changes of base flow in watersheds, particularly in low flow seasons is very important.
Methodology
Time series models are represented in three main forms: self-correlated models (AR), moving average (MA) models, and self-correlated and moving average (ARMA) models. The condition of using these models is the static nature of the used data. If the data is not static, the data series must be static with the existing methods. The existence of "I" in ARIMA indicates the non-static nature of the original data and the change in the data for modeling. If the data series has a cyclic and rotational state, the type of model is seasonal or SARIMA. Time series models have two components of (p, d, q) and s (P, D, Q). S (P, D, Q) is a seasonal component. P and q are respectively autoregressive parameters and non-seasonal moving average. P and Q are autoregressive parameters and seasonal moving average. The other parameters, D and d, are differential parameters for making the time series static.
Result
The statistical and probabilistic models have been presented and developed. This study aimed to analyze and compare the performance of series 30 and 56 years and monthly average discharge related to the Kakareza River in the Selsele city and the Kashkan Afrineh River in the Poldokhtar city in Lorestan province. To this end, the first climate in this region was determined. Next, the autocorrelation function and partial autocorrelation real data draws in XLSTAT software was done. Subsequently, the data was normalized using the Box-Cox and logarithmic. Then, the data trend that indicated non-stationary was determined. After that by using the different operator in MINITAB software, the data trend was removed and the suitable model with the lowest Akaike was selected. Then both periods 12 and 24 months for the two regions were simulated. Results showed that the selected models in 12 and 24 months periods had respectively a correlation coefficient of .92 and .86 for the kakareza river and .94, .88 for the Kashkan Afrineh river.
Discussion and conclusion
The most significant difference between the observed and the simulated values is in two months of Esfand and Farvardin. In addition, due to high precipitation, there was a significant increase in the amount of discharge in Farvardin. According to the climatic conditions in the study areas, the model showed a better performance in semi-arid areas.
