جستجوی مقالات مرتبط با کلیدواژه "پیش بینی سیل" در نشریات گروه "آبخیزداری، بیابان، محیط زیست، مرتع"

The most important issues of watershed management, is predicting hydrological rocesses. Using new models in this field can help to management and proper planning. In addition, predicting of river flow, especially in flood conditions, will allow the authorities to reduce flood damage with the preparation. One of the ways which have recently been used to predict and estimate the flow rate of rivers is neuro-fuzzy model. Neuro-fuzzy with review and determine the relationships between inputs and output, estimate the desired output deals. In this study, the three years values of the daily rainfall and discharge of different stations in Taleghan watershed were used as input to the neuro-fuzzy model and according to the statistical coefficients (RMSE, R2 and E), the best structure and inputs composition of neuro-fuzzy to predict the river flow was determined. Results demonstrated that the best estimates were of the Gaussian fuzzfier. Although different input modes, provided acceptable results, Best estimates with coefficients RMSE and R2 (training data 0.02 and 0.98-checking data 0.06 and 0.87), was discharge of Mehran and Joestan and previous day discharge of Garab and Dehdar. The results indicated that neuro-fuzzy can predict the daily flow with high accuracy and can be used in watershed management and flood control.

The uncertainty in forecasted precipitation remains a major source of uncertainty in real time flood forecasting. Precipitation uncertainty consists of uncertainty in the magnitude, temporal distribution, and spatial distribution of the precipitation. Due to uncertainty propagation of precipitation in flood forecasting model of HEC-1, temporal disaggregation method is applied by using the framework of fuzzy Extension Principle supported by a normal genetic algorithm. The uncertainty due to the unknown temporal distribution of the precipitation is achieved by randomly disaggregation of the precipitation into subperiods. Uncertainty in discharge and volume of flood hydrograph due to precipitation with temporal disaggregation and precipitation without temporal disaggregation is estimated and is compared with each others. The results show that in all forecasts the uncertainty in discharge and volume of flood hydrograph due to precipitation with temporal disaggregation is significantly more than uncertainty due to precipitation without temporal disaggregation. So that for forecast subperiod equal to six and membership function of precipitation magnitude equal to zero, the uncertainty in peak discharge due to precipitation with temporal disaggregation and without temporal disaggregation are 33. 7% and 16%, respectively. Also the uncertainty in volume of flood hydrograph due to precipitation with temporal disaggregation and without temporal disaggregation are 23. 5% and 14. 8%, respectively. Also the uncertainty in peak discharge due to the uncertainty in the temporal and spatial distribution can be significantly dominant over the uncertainty due to the uncertainty in the magnitude of the precipitation. So that for forecast subperiod equal to six and membership function of precipitation magnitude equal to zero، the uncertainty in peak discharge due to the temporal and spatial distribution and the magnitude of precipitation are 17. 7% and 16%, respectively. Therefore using space- and time-averaged precipitation over the basin may lead to erroneous forecasts.