Showing 3 results for Faghih
H Faghih, M Kholghi, S Kochekzadeh,
Volume 12, Issue 46 (1-2009)
Abstract
Overtopping is one of the main factors responsible for dam failure. To avoid overtopping, dam is equipped with one or some spillways to release the water impounded in the reservoir. The number and size of these spillways are determined on the basis of design flood. Determination of design flood of dam spillway can be formulated as a multiobjective risk problem. This problem can be solved by Quantitative Risk Analysis Methods. Here, four economical design methods which are based on risk analysis including, United States National Research Council (NRC), US Civil Engineering, Unit Curve and Partitioned Multiobjective Risk (PMR) were studied. In order to compare these methods, Risk Analysis was performed for re-determining design flood of Pishin Dam Spillway. This Dam has been constructed on the Sarbaz River. Owing to the fact that the integrals of the expected damage relations in the two methods, i.e., Civil Engineering, and Partitioned Multiobjective Risk are analytically unsolvable, Romberg numerical integration technique and Excel software were utilized for the related calculations and drawing graphs. Also, in order to select suitable distribution, the flood analysis was done using Smada software. The findings of the study indicated that design flood determined by the three methods, i.e., Civil Engineering, National Research Council and Unit Curve was almost the same, and that the amount of flood was less than the 10,000-year-old flood while design flood determined by Partitioned Multiobjective Risk Method, was larger than the 10,000- year-old flood.
H Faghih ,
Volume 14, Issue 51 (spring 2010)
Abstract
Estimating spatial distribution of precipitation is vital to execute water resources plans, drought, land-use plans environment, watershed management, and agricultural master plans. High variation in amount of precipitation in various parts, lack of measurement stations, and the complexity of relationship between precipitation and parameters affecting it have doubled the importance of developing efficient methods in estimating spatial distribution of precipitation. Artificial neural network has been proved to be efficient as a new way for modeling and predicting the processes for which no solution and explicit relationship has been available in accurately identifying and describing them. The purpose of this study is to investigate the efficiency of artificial neural network in estimating spatial monthly precipitation. To achieve this objective, neural network with multilayer perceptorn topology was employed for preparing model for spatial monthly precipitation in five synoptic and rain-gauge stations located in Kurdistan province. In order to design the topology of the model in each station, as the adjustable parameters (including transfer function, learning rule, amount of momentum, number of hidden layers, number of neurons of the hidden layers, and the number of epochs) changed, different neural networks were made and carried out. In each case, the topology with the minimum amount of root mean square error (RMSE) was selected as the optimal model. Owing to the fact that the selection of each of the variable parameters of neural network necessitated recurring trails and errors, and consequently teaching a large number of networks with various topologies, genetic algorithm method was utilized for finding the optimization of these parameters the efficiency of this method, too, was examined in terms of the optimization of neural network. The findings indicated that neural network enjoys a high degree of accuracy in modeling and estimating spatial distribution of monthly precipitation. In addition, combining it with genetic algorithm method was positively evaluated in optimizing the requirements for executing neural network. In most cases, mixed method proved its superiority over executing neural network without optimization. The most precise model in all of the stations under study was achieved by the use of transfer function, sigmoid, learning rule of Levenberg Marquardt in the selected models, the determination coefficient (R2) observed between the model output amounts and the data observed in station were found to be 0.86 0.89 0.94 0.77 and 0.94.
H. Faghih, J. Behmanesh, K. Khalili,
Volume 22, Issue 1 (Spring 2018)
Abstract
Precipitation is one of the most important components of water balance in any region and the development of efficient models for estimating its spatiotemporal distribution is of considerable importance. The goal of the present research was to investigate the efficiency of the first order multiple-site auto regressive model in the estimation of spatiotemporal precipitation in Kurdistan, Iran. For this purpose, synoptic stations which had long time data were selected. To determine the model parameters, data covering 21 years r (1992-2012) were employed. These parameters were obtained by computing the lag zero and lag one correlation between the annual precipitation time series of stations. In this method, the region precipitation in a year (t) was estimated based on its precipitation in the previous year (t-1). To evaluate the model, annual precipitation in the studied area was estimated using the developed model for the years 2013 and 2014; then, the obtained data were compared with the observed data. The results showed that the used model had a suitable accuracy in estimating the annual precipitation in the studied area. The percentages of the model in estimating the region's annual precipitation for the years 2013 and 2014 was obtained to be 7.9% and 17.3%, respectively. Also, the correlation coefficient between the estimated and observed data was significant at the significance level of one percent (R=0.978). Furthermore, the model performance was suitable in terms of data generation; so the statistical properties of the generated and historical data were similar and their difference was not significant. Therefore, due to the suitable efficiency of the model in estimating and generating the annual precipitation, its application could be recommended to help the better management of water resources in the studied region.
