Showing 12 results for Vafakhah
M. Vafakhah, G.h. Shojaei,
Volume 11, Issue 42 (winter 2008)
Abstract
Continuous measurement of river discharge is a hard and expensive task in hydrology. To overcome this problem, the stage readings at hydrometric gauges are permanently taken and the discharge of any time at which the actual discharge is unavailable will be estimated through a relationship between discharge and stage. To study the stage-discharge relations and the capability of long-term data in establishing a permanent stage-discharge relationship, and also to determine the best time to measure the discharge of rivers, a study was conducted at the hydrometric station of the Zayandehrud regulatory dam using data from 1990 to 2003. The data were analyzed using simple regression analysis, the percentage of relative error and factor analysis. The results indicated that the best model to show the stage-discharge relation at the studied station is a power function model. Moreover, the model used for every year can only be used for that year. The results also showed that the most suitable times for the measurement of discharge are July, December and March.
H.r. Moradi, M. Vafakhah , A. Akbari Baviel,
Volume 11, Issue 42 (winter 2008)
Abstract
Discharge routing as a mathematic process to forecast the changes of greatness, speed and form of flood wave is function of time in one or more points along drainage, canal or reservoir. Hydrologic and hydraulic methods are used to accomplish the flood routing. Although hydrologic method is less accurate than hydraulic methods but it is simpler to use in flood control and designing works with acceptable confidence. This study has been conducted for flood routing in Lighvan River from Lighvam hydrometric station to Hervy hydrometric station in eastern Azerbaijan Province in a distance the 12 Km. The discharge for different return periods (2, 10, 20, 50 and 100 years) was calculated by using upstream stations data. Then routing of every flood discharge was studied with different return periods by Muskingum and Muskingum-Cunge methods. Results showed significant difference between calculated discharges routing by two methods with discharge values to relate that return period in Hervy hydrometric station. The reasons of exist this different, is numerous for example mountain location this area and to exist sub drainage in between two stations and etc.
Y. Nabipoor, M. Vafakhah, H. R. Moradi,
Volume 18, Issue 67 (Spring 2014)
Abstract
The occurrence trend of floods in recent years shows that the most of Iran regions located in attacks of destructive floods and loss of life and property of flood damages is increasing. Watershed management practices (WMPs) are one of the superior and appropriate solutions for flood hazards mitigation. The impact of WMPs can be investigated using different approaches. In this study, the direct impact of WMPs was investigated using quantitative evaluation of flood characteristics for two periods, pre and post periods of measures implementation. Therefore, daily hydrograph of investigated periods and the results of flood analyses including number of floods occurrence, flood frequency percent in the different months and seasons were determined in Hajighoshan and Tamar hydrometery stations. Also, the mean continuing, rise and subsidence time of floods and maximum peak discharge of observed floods were investigated. The research results showed that the occurrence trend of floods had relatively increased. The number of floods has increased in post periods of measures implementation in two hydrometery stations, while WMPs effect on all flood characteristics were positive, as the continuing time of floods has increased with 0.5%, rise and subsidence time of floods and maximum peak discharge of floods have decreased with 7.9%, 21.98% and 70%, respectively. Totally, if WMPs volume pre watershed area isn't low, WMPs effect on flood characteristics will be positive.
S. Razavizadeh, A. Kavian, M. Vafakhah,
Volume 18, Issue 68 (summer 2014)
Abstract
Prediction of sediment load transported by rivers is a crucial step in the management of rivers, reservoirs and hydraulic projects. In the present study, in order to predict the suspended sediment of Taleghan river by using artificial neural
network, and recognize the best ANN with the highest accuracy, 500 daily data series of flow discharge on the present day, flow discharge on the past day, flow depth and hydrograph condition (respectively with the average of 13.83 (m
3/s), 15.42 (m
3/s), 89.83 (cm) and -0.036) as input variables, and 500 daily data series of suspended sediment, as the output of the model were used. The data was related to the period of 1984-2005. 80 different neural networks were developed using different combinations of variables and also changing the number of hidden-layer neurons and threshold functions. The accuracy of the models was then compared by R
2 and RMSE. Results showed that the neural network with 3-9-1 structure and input parameters of flow discharge on the present day, flow discharge on the past day and flow depth was superior (R
2= 0.97 and RMSE= 0.068) compared to the other structures. The average of the observed data of sediment and that predicted by the optimal model (related to test step) were 1122.802 and 1184.924 (tons per day), respectively.
N. Dehghani, M. Vafakhah,
Volume 18, Issue 69 (fall 2014)
Abstract
Sediment is as one of problems related to water resources utilization. Numerous formulas have been developed for bed load estimation in rivers. In this study, eleven common formulas including hydrologic and hydraulic methods such as Einstein Tofalti Meyer, Peter and Muller Van Rayne Modified Van Rayne Yalin Bagnold Fraylink Habibi and Sivakumar and Samaga were used for selection of the most suitable bed load estimator formula in Kharajgil hygrometry station on Navrud river. The results showed that Habibi and Sivkumar formula is the most suitable with the mean computed to observed data=1.35, standard deviation computed to observed=1.96, RMSE=1.63 and ill sorted ratio(computed transportation ratio to observed)=33.82% within the range of 0.5 to 2.
N. Dehghani , M. Vafakhah, A. R. Bahremand,
Volume 19, Issue 73 (fall 2015)
Abstract
Rainfall-runoff modeling and prediction of river discharge is one important parameter in flood control and management, hydraulic structure design, and drought management. The goal of this study is simulating the daily discharge in Kasilian watershed by using WetSpa model and adaptive neuro-fuzzy inference system (ANFIS). The WetSpa model is a distributed hydrological and physically based model, which is able to predict flood on the watershed scale with various time intervals. The ANFIS is a black box model which has attracted the attention of many researchers. The digital maps of topography, land use, and soil type are 3 base maps used in the model for the prediction of daily discharge while intelligent models use available hydrometric and meteorological stations' data. The results of WetSpa model showed that this model can simulate the river base flow with Nash- Sutcliff criteria of 64 percent in the validation period, but shows less accuracy with flooding discharges. The reason for this result can be the small and short Travel time noted. This model can simulate the water balance in Kasilian watershed as well. The sensitivity analysis showed that groundwater flow recession and rainfall degree-day parameters have the highest and lowest effect on the results, respectively. Also, ANFIS with the inputs of rainfall 1-day lag and evaporation 1-day lag, with Nash-Sutcliff criteria of 80, was superior to WetSpa model with Nash-Sutcliff criteria of 24 percent in the validation period.
M. Jafari, M. Vafakhah, A. Tavasoli,
Volume 19, Issue 73 (fall 2015)
Abstract
The rainfall-runoff process and flooding are hydrological phenomena that are difficult to study due to the influence of different parameters. So far, different methods and models have been provided to analyze these phenomena. The purpose of this study is evaluation of adaptive neuro-fuzzy inference system (ANFIS) for storm runoff coefficient forecasting. To that end, Barariyeh watershed was chosen in Neishabour and the data of 33 events were collected from 1952 to 2006. Factor analysis (FA) was used for determination of independent variables in storm runoff coefficient forecasting. Four variables were selected as independent variables, including average rainfall, third, first and fourth quartiles of rainfall intensity and also five other variables included &phi index and first to fourth quartiles of rainfall intensity. Other variables combined based on their hydrological role were considered as ANFIS inputs. The results revealed that the ANFIS inputs including first to fourth quartiles of rainfall intensity, &phi index, and total rainfall of five days before can predict storm runoff coefficient with R2=0.91, RMSE=0.02506, MAE=0.0666 and CE=0.87.
E. Shrifi Garmdareh, M. Vafakhah, S. Eslamian,
Volume 23, Issue 1 (Spring 2019)
Abstract
Flood discharge estimation with different return periods is one of important factors for water structures design and installation. On the other hand, a lot of rivers existing in Iran watersheds have no complete and accurate hydrometric data. In these cases, one of the suitable solutions to estimate peak discharges with different return periods is the regional flood analysis. In this research, 55 hydrometric stations were used. For this purpose, at first, peak discharges in different return periods were estimated using the EasyFit software. Then, the effective variables on the peak discharges were collected and the input variables of the models were selected by using gamma test with the help of the WinGamma software. Finally, data modeling was performed using the support vector machine, artificial neural networks and nonlinear multivariate regression techniques. Quantitative and qualitative assessment of the results using various indices including Nash-Sutcliffe Efficiency Coefficient (NSC) showed that SVM modeling method had the most accuracy in comparison to the other two modeling methods to predict the peak discharges in the Namak Lake Watershed.
K. Ghaderi, B. Motamedvaziri, M. Vafakhah, A.a. Dehghani,
Volume 25, Issue 4 (Winiter 2022)
Abstract
Proper flood discharge forecasting is significant for the design of hydraulic structures, reducing the risk of failure, and minimizing downstream environmental damage. The objective of this study was to investigate the application of machine learning methods in Regional Flood Frequency Analysis (RFFA). To achieve this goal, 18 physiographic, climatic, lithological, and land use parameters were considered for the upstream basins of the hydrometric stations located in Karkheh and Karun watersheds (46 stations with a statistical length of 21 years). The best Probability Distribution Function (pdf) was then determined using the Kolmogorov-Smirnov test at each station to estimate the flood discharge with a return period of 50-year using maximum likelihood methods and L-moments. Finally, RFFA was performed using a decision tree, Bayesian network, and artificial neural network. The results showed that the log Pearson type 3 distribution in the maximum likelihood method and the generalized normal distribution in the L moment method are the best possible regional pdfs. Based on the gamma test, the parameters of the perimeter, basin length, shape factor, and mainstream length were selected as the best input structure. The results of regional flood frequency analysis showed that the Bayesian model with the L moment method (R2 = 0.7) has the best estimate compared to other methods. Decision tree and artificial neural network were in the following ranks.
F. Daechini, M. Vafakhah, V. Moosavi, M. Zabihi Silabi,
Volume 26, Issue 2 (ُSummer 2022)
Abstract
Surface runoff is one of the most significant components of the water cycle, which increases soil erosion and sediment transportation in rivers and decreases the water quality of rivers. Therefore, accurate prediction of hydrological response of watersheds is one of the important steps in regional planning and management plans. In this regard, the rainfall-runoff modeling helps hydrological researchers, especially in water engineering sciences. The present study was conducted to analyze the rainfall-runoff simulation in the Gorganrood watershed located in northeastern Iran using AWBM, Sacramento, SimHyd, SMAR, and Tank models. Daily rainfall, daily evapotranspiration, and daily runoff of seven hydrometric stations in the period of 1970-2010 and 2011-2015 were used for calibration and validation, respectively. The automated calibration process was performed using genetic evolutionary search algorithms and SCE-UA methods, using Nash Sutcliffe Efficiency (NSE) and root mean of square error (RMSE) evaluation criteria. The results indicated that the SimHyd model with NSE of 0.66, TANK model using Genetic Algorithm and SCE-UA methods with NSE of 0.67 and 0.66, and Sacramento model using genetic algorithm and SCE-UA methods with NSE of 0.52 and 0.55 have the best performance in the validation period.
S. Ayoubi Ayoublu, M. Vafakhah, H.r. Pourghasemi,
Volume 26, Issue 3 (Fall 2022)
Abstract
Population growth, urbanization, and land use change have increased disastrous floods. Iran is also among the countries at high risk of floods. The latest examples of flood damage are the devastating floods of the spring of 2019 with significant mortality and financial losses in more than ten provinces of the country. The purpose of this study is to prepare an urban flood risk map of District 4 City Shiraz. The vulnerability of the region was made using PROMETHEE Ⅱ and COPRAS multi-criteria decision-making models and urban flood hazard zones were prepared by partial least squares regression (PLSR) and ridge regression (RR) models and a risk map was obtained by multiplying the vulnerability and hazard in ArcGIS software. The highest percentage of the study area in the PROMETHEE Ⅱ and COPRAS models belongs to the moderate class of vulnerability. The evaluation of the vulnerability models using Boolean logic and RMSE and MAPE statistics, showed that the COPRAS model provided better results than the PROMETHEE model. The results of partial least square regression (PLSR) and ridge regression (RR) models in flood risk modeling were analyzed by the Taylor diagram, which showed the superiority of the ridge regression (RR) model and the accuracy of this model in preparing urban flood hazard maps. The risk map of the study area indicated that 34% of the area (973 ha) is in the range of high and very high flood risk.
F. Esmaeili, M. Vafakhah, V. Moosavi,
Volume 27, Issue 1 (Spring 2023)
Abstract
Digital elevation models (DEMs) are one of the most important data required in watershed modeling with hydrological models and their spatial resolution has a significant impact on the accuracy of simulating hydrological processes. In the present study, the effect of spatial resolution of five DEMs derived from the topographic map (TOPO) with a scale of 1:25000, ALOS PALSAR, ASTER, SRTM, and GTOPO with a spatial accuracy of 10, 12.5, 30, 90, and 1000 m, respectively, on the estimation of parameters of geomorphological and geomorphoclimatic unit hydrographs models has been evaluated in Amameh watershed. Thirty-four single flood events were used during the years 1970 to 2015. The results showed that in the GUH method, the application of the TOPO and ALOS PALSAR DEMs had the best results with root mean square error (RMSE) of 1.7 and 1.8 m3/s and Nash-Sutcliffe Efficiency (NSE) of 0.4 and 0.3, respectively. While the GTOPO DEM had the least efficiency with RMSE of 2.8 m3/s and NSE of -2. Similarly, the lowest and highest RMSE in the GCUH method belonged to TOPO and GTOPO DEMs with RMSE of 3.8 and 18 m3/s and NSE of 0.2 and -6, respectively. Generally, the GUH method had more favorable results than the GCUH method in all DEMs.
