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Showing 3 results for Izadbakhsh

M.a. Izadbakhsh, S.s. Eslamian, S.f. Mosavi,
Volume 5, Issue 2 (summer 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%.


F. Hayati, A. Rajabi, M. Izadbakhsh, . S. Shabanlou,
Volume 25, Issue 1 (Spring 2021)
Abstract

Due to drought and climate change, estimation and prediction of rainfall is quite important in various areas all over the world. In this study, a novel artificial intelligence (AI) technique (WGEP) was developed to model long-term rainfall (67 years period) in Anzali city for the first time. This model was combined using Wavelet Transform (WT) and Gene Expression Programming (GEP) model. Firstly, the most optimized member of wavelet families was chosen. Then, by analyzing the numerical models, the most accurate linking function and fitness function were selected for the GEP model. Next, using the autocorrelation function (ACF), the partial autocorrelation function (PACF) and different lags, 15 WGEP models were introduced. The GEP models were trained, tested and validated in 37, 20- and 10-years periods, respectively. Also, using sensitivity analysis, the superior model and the most effective lags for estimating long-term rainfall were identified. The superior model estimated the target function with high accuracy. For instance, correlation coefficient and scatter index for this model were 0.946 and 0.310, respectively. Additionally, lags 1, 2, 4 and 12 were proposed as the most effective lags for simulating rainfall using hybrid model. Furthermore, results of the superior hybrid model were compared with GEP model that the hybrid model had more accuracy.

Y. Esmaeli, F. Yosefvand, S. Shabanlou, M.a. Izadbakhsh,
Volume 27, Issue 2 (Summer 2023)
Abstract

The objective of the current study was to zone flood probability in the Marzdaran watershed. Since the allocated budget for management work is limited and it is not possible to carry out operations in the whole area, having a map that has prioritized different areas in terms of the probability of flood occurrence will be very useful and necessary. A well-known data mining model namely MaxEnt (ME) is applied due to its robust computational algorithm. Flood inventories are gathered through several field surveys using local information and available organizational resources, and the corresponding map is created in the geographic information system. The twelve predisposing variables are selected and the corresponding maps are generated in the geographic information system by reviewing several studies. The area under the curve (ROC) is used to evaluate the modeling results. Then, the most prone areas of flood occurrence which are prioritized for management operations are identified based on the prepared map. Based on the results, about 100 km2 of the study area is identified as the most prone area for management operations. The results showed that the accuracy of the maximum entropy model is 98% in the training phase and 95% in the validation phase. The distance from the river, drainage density, and topographic wetness index are identified as the most effective factors in the occurrence of floods, respectively.


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