جستجوی مقالات مرتبط با کلیدواژه "غلظت رسوب" در نشریات گروه "مهندسی آب"

It is important to predict debris flood for reducing its damages. The aim of this study is the prediction of sediment concentration of debris floods and ordinary floods using bayesian network (BN) and artificial neural network (ANN) models in Ammameh, Navrood and Casilian basins which were located in Tehran, Gilan and Mazandaran provinces, respectively. Accordingly, average basin elevation (EL), average basin slope (S), watershed area (A), current day rainfall (R), antecedent rainfall (AR) of three-days ago and discharge of one-day ago were selected as input variables. Then, 32 scenarios were tested to determine the most effective factors on the sediment concentration of flood. For the scenario derived from all selected factors, indices R2 and MAPE in the test stage were obtained 0.97 and 8.55%, respectively. Assessment of the effect of different factors shows that the most effective factors on the BN model’s prediction accuracy are EL, R, PQ, A and AR one-day ago. Indices R2 and MAPE for this scenario were obtained 0.916 and 11.01%, respectively. It was selected as the best scenario because the least number of predictors and the highest accuracy. The most effective factors identified in this study can be used to predict debris flood in similar basins.

The present study conducted for performance comparsion of suspended sediment estimation methods to data classification method and also comparsion of abservation and estimation sediment in all methods for thre stations of Doab Merk and Shirgah and Krikla. The results showed that the normal rating curve using hydroseason introducted as optimation model in Dooab Mark stationwith the lowest root mean square error (700.96). While, the Shirgah and Krikla stations the CF2 method using calendar method had better performance with root mean square error of amount of 12074.648 and 559.73, respectively. But, FAO model had not high performance in three stations using three applied methods. Also, the compersion of estimated and observed sediment in Shirgah and Karikla stations using hydocimate and without data classification, the CF2 modified method and the normal sediment rating curve had the better estimation with different percent with different percent of -9.56% and -6.17%, respectively. While, the results showed that for the Krikla station calendar and hydroclimatic classification, the CF2 modified method had the best estimation with different percent of -22.33 and -22.55%, respectively. The conclusion showed that the changing of climate region in studied stations cased the changing of optmimal models in estimated suspended sediment.