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Showing 3 results for Flood Modeling

M.a. Izadbakhsh, S.s. Eslamian, S.f. Mosavi,
Volume 5, Issue 2 (7-2001)
Abstract

Flood is one of the catastrophic events that has attracted the hydrologists’ attention. In this research one of the important flood indices, i.e. maximum-daily mean-discharge, was determined for several western Iran watersheds, namely, in the catchments of Gamasiab, Qarasou, Saimare, Kashkan, Sezar and Abshineh. Daily data were prepared from stream-gauging stations and a 30-year concurrent period was selected.

 Flood frequency analysis was performed using HYFA and TR computer programs and optimum distributions were chosen by goodness of fit tests. Extreme flow values having different return periods of 2, 5, 10, 25, 50, 100, 500 and 1000 years were calculated. Modeling was done with regional analysis using multiple regression technique between maximum-daily mean-discharge and physiographic characteristics of the basins. The most important parameter for the selection of the model was the adjusted coefficient of determination while significant level, standard error and observed discharger vs. computed discharge plot acted as controlling parameters. Finally, different models with different parameters were selected from power, exponential, linear and logarithmic forms. The results showed the power model to be the best among the four types. The main channel length, drainage density and time of concentration were the most effective parameters on flow. After analyzing the errors, it appeared that increasing the return period would cause an increase in the model error. At 1000-year return period, the error reached 32.2%.


M. T. Dastorani,
Volume 11, Issue 40 (7-2007)
Abstract

The potential of artificial neural network models for simulating the hydrologic behaviour of catchments is presented in this paper. The main purpose is the modeling of river flow in a multi-gauging station catchment and real time prediction of peak flow downstream. The study area covers the Upper Derwent River catchment located in River Trent basin. The river flow has been predicted (at Whatstandwell gauging station) using upstream measured data. Three types of ANN were used for this application: Multi-layer perceptron, Recurrent and Time lag recurrent neural networks. Data with different lengths (1 month, 6 months and 3 years) have been used, and flow with 3, 6, 9 and 12 hours lead-time has been predicted. In general, although ANN shows a good capability to model river flow and predict downstream discharge by using only upstream flow data, however, the type of ANN as well as the characteristics of the training data was found as very important factors affecting the efficiency of the results.
P. Mohit-Isfahanii, V. Chitsaz,
Volume 27, Issue 1 (5-2023)
Abstract

Introducing reliable regional models to predict the maximum discharge of floods using characteristics of sub-basins has special importance in terms of flood management and designing hydraulic structures in basins that have no hydrometric station. The present study has tried to provide appropriate regional flood models using generalized linear models (GLMs) to estimate 2-, 10-, 50-, and 100-year maximum daily discharges of 62 sub-basins in Great-Karoon and Karkhe basins. According to the results, the sub-basins were categorized into four sub-regions based on some physiographic and climatic characteristics of the study sub-basins. The results showed that regional flood modeling was successful in all sub-regions except sub-region II, which includes very large basins (A̅≈17300 km2). The adjusted R2 of the best models in sub-regions I, III, and IV were estimated at around 82.4, 91.3, and 90.6 percent, and these models have a relative error (RRMSE) of around 9.5, 9.23, and 6.7 percent, respectively. Also, it was found that more frequent floods with 2- and 10-year return periods are influenced by properties such as basin’s length, perimeter, and area, while rare floods with 50- and 100-year return periods are mostly influenced by the river systems characteristics such as the main river length, total lengths of the river system, and slope of the main river. According to the research, it can be stated that the behavior of maximum daily discharges in the study area is extremely influenced by the different climatic and physiographic characteristics of the watersheds. Therefore, the maximum daily discharges can be estimated accurately at ungauged sites by appropriate modeling in gauged catchments.


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