جستجوی مقالات مرتبط با کلیدواژه « پیش بینی جریان روزانه » در نشریات گروه « آب و خاک »

Accurate estimation of river discharge is an important issue in forecasting of drought and floods, designing of water structures, dam reservoir operation and sediment control. So far, several methods such as time series models, Artificial Neural Networks, Fuzzy models and Genetic programming have been used for accurate predicting of river flow. In this study, Genetic Programming and Bayesian Networks methods were used to forecast daily discharge of Barandoozchay River. The daily discharge data of Barandoozchay River measured at the Dizaj hydrometric station during 2007 to 2011 was used for modeling, which 80% of the data used for training and remaining 20% used for testing of models. For assessing the role of memory in increasing or reducing of model accuracy, we tested different combinations of input variables. The results showed that at first, the accuracy of models increase with increasing of memory, as the most accuracy obtained in third combination of input variables in both of methods. After that with increasing of memory the accuracy of models decreased. Comparing the performance of GP and BNs models indicated that the accuracy of the GP method with the R=0.978 and RMSE=1.66 (m3/s) was slightly more than BNs method with R=0.964 and RMSE=1.96 (m3/s). In addition, the performance of GP method was better than BNs method in predicting minimum and average discharges.

Accurate estimation of river discharge is one of the important steps to optimum use of water resources. In this study, Support Vector Machines (SVM) and Bayesian Networks (BNs) methods were used to forecast daily discharge of Barandoozchay River. The daily discharge data of Barandoozchay River measured at the Dizaj hydrometric station during 2006 to 2010 was used for modeling, which 80% of the data used for training and remaining 20% used for testing of models. For assessing the role of memory in increasing or reducing of model accuracy, we tested different combinations of input variables. The results showed that at first, the accuracy of models increased with increasing of memory, as the most accuracy obtained in third combination of input variables in both of the methods. After that, with increasing of memory, the accuracy of models decreased. Comparing the performance of SVM and BNs models indicated that the accuracy of the SVM method with the R=0.976 and RMSE=1.80 (m3/s) was slightly more than BNs method with R=0.964 and RMSE=1.96 (m3/s).

Accurate forecasting of the daily discharge plays a significant role in the efficient management of water resources. For this purpose in order to model more accurately the process of forecasting the daily discharge of Nooranchay river in Atashgah basin, the artificial neural networks model (ANN) was used. In addition, in order to increase the accuracy of ANN, the principal component analysis (PCA) was used for preprocessing of input data. Finally, the results of multivariate linear regression (MLR) model were compared with the obtained results in the mentioned hydrological simulation. The results indicated that the hybrid model of ANN-PCA in comparison with ANN and MLR, had the highest precision. So that the results of goodness-of-fit tests criteria, such as the correlation coefficient (CC), the efficiency coefficient (EC) and the root mean square error (RMSE) for the hybrid model of ANN-PCA (at the verification stage) were CC=0.9959, EC=0.9905 and RMSE=0.0071, and for the ANN (at the verification stage) were CC=0.9093, EC=0.8269 and RMSE=0.0405 and the results for the MLR were obtained as CC=0.8866, EC=0.7860 and RMSE=0.0926. Also the use of PCA as an effective method for pre-processing of data, created independent components which eliminated the multicollinearity. Therefore, the PCA increased the efficiency of the ANN.

Accurate estimation of river flow can have a significant importance in water resources management. In this study, Genetic programming (GP) and Support Vector Machine (SVM) methods were used to forecast daily discharge of Barandoozchay River. The daily discharge data of Barandoozchay River measured at the Dizaj hydrometric station during 2007 to 2011 was used for modeling, which 80% of the data used for training and remaining 20% used for testing of models. The results showed that in the both of considered methods, the models including discharges of one, two and three days ago had higher accuracy in verification step and the accuracy of models decreased with increasing discharge values. Comparing the performance of GP and SVM methods indicated that, however the accuracy of the GP method with the R=0.978 and RMSE=1.66 (m3/s) was slightly more than SVM method with R=0.976 and RMSE=1.80 (m3/s), but the SVM is easier than GP method. Thus, the SVM method can be used as an alternative method in forecasting daily river discharge.

One of the applicable ways for simulation and forecasting hydrological processes is time series modeling. An important problem in forecasting hydrological data using time series is generating stochastic data. Any changes in stochastic series will change generating data. In this study nonlinear ARCH model presented in order to modeling and generating stochastic component of time series. After combing ARCH model with nonlinear bilinear model، BL-ARCH model suggested to forecasting river flow discharge. Daily river flow of Shahar-Chai River located in the west of Urmia Lake and West Azarbaijan province have been used for data analysis and 11 years forecasting. As results shown suggested model with 4. 52 error has better than bilinear model with 6. 77 error. So this model can be used for short-time river flow forecasting specially daily series.

The effect of internal and external forces on the transmission and distribution networks of potable municipal water makes more problems in the Iran water and waste water Corporation. Therefore، analyzing loaded forces is very important. Pipe insertion in the trenches، the channel filler material tend to have subsidence because of its reload and the channel subsidence on its bed causes trenches and the soil column above the channel move downward compared to adjacent undisturbed soils. The mentioned phenomena and its modeling are not considered along with many water pipeline designs. Thereafter، the current case study is presented. In order to evaluating this research، the data of flexible steel pipeline with large diameter is gathered from Mashhad Abfay company and its modeling is done with 5 atmospheres internal pressure in Ansys finite element software. The more credibility is satisfied with selecting the current elements of Ansy’s Plane 42 and Plane 82. The output results showed 8/5 mm mean subsidence in the pipe and also 5 cm in the asphalt surface. Amount of subsidence in the element Plane 42 was about 0/6 mm less than average. Subsidence process was similar in both elements and was in accordance with the assumptions. In one hand، Plane 82 element was found that due to damping by considering the characteristics and volume of soil، more efficient than other element in determining the substrate deformation behavior and around the tube and on the other hand، the flexible pipes are tolerant to deformation and its preventive force against to stress concentration is higher than rigid channels.