Mitra Tanhapour; Mohammad Ebrahim Banihabib
Abstract
Debris flow is one of the natural hazards that threats people's lives in the mountainous populated areas. Thus, it is necessary to determine the rainfall thresholds for debris flow occurrence in order to develop an effective forecasting system. In this study, the empirical thresholds of rainfall for ...
Read More
Debris flow is one of the natural hazards that threats people's lives in the mountainous populated areas. Thus, it is necessary to determine the rainfall thresholds for debris flow occurrence in order to develop an effective forecasting system. In this study, the empirical thresholds of rainfall for the occurrence of debris flow by Hirano method were assessed in a part of Alborz mountainous basins including Gorganrood, Navrood, Neka and Babolrood. For this purpose, the rainfall hyetographs of recording rain gauges were used from the period of 1983-2004. Then, the intensity-duration rainfall thresholds (I-D threshold) for selected basins were estimated and compared with previous studies from the local, regional and global scale. The examination of rainfall thresholds for the initiation of debris flow showed that the rainfall of more than 27.2 and 14.8 mm, respectively, in the Navrood and Gorganrood watersheds and rainfall more than 37.84 and 66.12 mm, respectively, in the Babolrood and Neka basins are able to trigger debris flow during their concentration time. Comparison of the I-D threshold of this study with the results of previous studies showed I-D threshold of the studied basins generally are lower than the thresholds of local and regional but higher than global thresholds. In other words, there are some areas in the world that need smaller rainfalls for initiation debris flow in comparison to the study area. The difference among threshold of debris flow occurrence in the world’s basins comes from their variety in climatic, geographical, physiographic and geological factors.
Mitra Tanhapour; Mohammad Ebrahim Banihabib
Abstract
Prediction of the sediment load in water resources engineering projects such as flow diversion projects and dam construction is important factor for determining their service life. In this study, a model for estimation of daily sediment discharge was proposed using multilayer perceptron Artificial Neural ...
Read More
Prediction of the sediment load in water resources engineering projects such as flow diversion projects and dam construction is important factor for determining their service life. In this study, a model for estimation of daily sediment discharge was proposed using multilayer perceptron Artificial Neural Network (ANN) model with back-propagation learning algorithm. For this purpose, current day’s discharge (Qt), precipitation, number of day in the year (DOY) and previous day’s discharge (Qt-1) data of Zoghal Bridge station (located on Chalus River) from 1990 to 2009 were used for training, verification and test. Results of testing different combinations of input data sets showed that effective parameters of the model performance are current discharge parameter, antecedent discharge, precipitation and DOY, respectively. This results has a relatively good agreement with standardized coefficients of regression model. Coefficient of determination (R2) and Root Mean Square Error (RMSE) were used to compare the different structures of ANN. Therefore, best network with 3-5-1 architecture and the amounts of R2=0.89 and RMSE=0.02 was obtained by elimination of DOY variable. The performance of ANN model in the prediction of sediment discharge was compared with Sediment Rating Curve (SRC) and Multiple Non-Linear Regression (MNLR) model. The results showed, in the training and test steps, SRC method and ANN model have the best performance, respectively. Furthermore, in the test step, the ANN model performed better results compared to two other methods by increasing R2 about 16%. Generally, the proposed ANN model can be estimated sediment discharge by less calculation time and cost and also with more accuracy.
