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Mr Arsalan Mehrvarz, Dr Agil Madadi, Dr Fariba Esfandyari, Mr Masoud Rahimi,
Volume 0, Issue 0 (3-1921)

The morphology of river is one of the issues in geomorphology, engineering and river management. The marginal sections of the rivers have always been subject to the riverbed due to the special social and economic conditions. The Dare Ourt River is one of the permanent rivers in Ardabil province. In recent years, there have always been floods and changes in the morphology of the duct. In this study, the Dare Ourt river was analyzed using Rosegen model at levels one and two. The main data required for this study include: 1: 2000 river topographic maps, hydrometric data and boundary conditions (Ardabil Regional Water Authority). The HEC-RAS hydrodynamic model was also used to more accurately extract the main indices of the Rosegen model. The results showed that most parts of the river have a C6c type with alluvial substrate and other dominant types observed in the four studied ranges include B6c-E6b-F6-D types. Also, the results of field visits indicate a change in the type of river type D range 4 from type C to type F, which is difficult due to the fact that reconstruction and restoration of the river in type F is difficult. Recommended restrictions on the type F is prevented from turning the river.
Aghil Madadi, Elnaz Piroozi,
Volume 16, Issue 42 (12-2016)

The purpose of this study is erosion and sediment is estimated in whole of Lay watershed, located in the North Wast of Iran.In this study, the model of WEPP, was provided for simulation and forecast of erosion and sedimentation in the study watershed. The data required for WEPP model are entered in six files, including soil file, management, climate, slope, channel and pounds files. In this study, after determining of work units, the information for developing file were collected. In this region there are, 3 types of soil, 2 managements  and 5 channel types. After making the files, WEPP model was run via Geowepp software. It is notable that in this software water erosion and sediment amount was estimated by three methodes of hillslope, watershed and fow path. In these methods sediments estimated were 0.308, 0.215, 0.491 tha-1 y-1, respectively. According to estimated results, the hillslpe with 0.308  tha-1 y-1 was in good agreement with actual amount 0.319 and is suitable for the erosion and sediment of  Lay watershed.

Dr Sayyad Asghari, Roholah Jalilyan, Dr Noshin Pirozineghad, Dr Aghil Madadi, Milad Yadeghari,
Volume 20, Issue 58 (10-2020)

Water is one of most important human needs for life. According to importance of subject, discussion of management and utilization of water resources has become one of the most important global issues. Remote sensing data are often used in water body extraction studies and type of remote sensing data used plays an important role in water body extraction. In this study, ability of Landsat satellite with application of water indices, to extraction of Gamasiab River in Kermanshah and comparing these indices have been investigated. Specific feature of Low width and shallow rivers has increased the complexity of studies of such rivers using available data. Water body extraction from remote sensing images has been over the past two decades. Water indices were first developed using Landsat TM and Landsat ETM. But its better performance in Landsat 8 is well documented by the researchers. In this study, NDWI, MNDWI, AWEI_nsh, AWEI_sh and WRI indices were used. With extracting optimal threshold from histogram of indices and applying this threshold, the study area was classified into two classes of water and non-water. Then overall accuracy and kappa coefficient values were taken from each of the indices. Finally, AWEI index with overall accuracy of 99.09% and a Kappa coefficient of 0.98 was the best response among the indices in the study area. The results this study showed that approach can easily extract water from satellite imagery.

Aghail Madadi, Ebrahim Beheshti Javid, Nazfar Aghazadeh,
Volume 21, Issue 62 (10-2021)

During the last years, following an increase in damages due to occurrence of landslides, human has decided to reduce such losses. Therefore, identifying regions susceptible to landslide and classifying them can partly help man to prevent from happening above phenomena. The current study aims to demonstrate factors contributing to occurrence of landslide in Candrigh Chay basin and then, flattening the basin regarding risks related to landslide event using one methods, Network Analysis Process. In this direction, as respects to basin morphology and also, findings of past researches , ten factors involved in occurring landslides in Candrigh Chay Basin were recognized and used : lithology , land use , rainfall , slope , slope aspect , road slide , sluice power index (SPI), sediment transfer index (STI).Network Analysis Process(ANP) Method was implemented to score and classify factors and  scales. Was carried out in order to measure classes of each parameter. The final map showed that the basin has 4 classes considering landslide. Findings reveal flats with low risk and flats with high risk have the least area in Candrigh Chay basin. On the other hand, flats with medium risk and high risk allocate the most area of basin for themself. Comparing slid surfaces with flats facing risks indicates regions located at high and average risk class possess most areas of slide surfaces so that a flat involving very high risk  and a flat with high risk devote 35 percent (79km²) and 32/6 (72km²) of landslides for themselves, respectively. In other words, more than 77 percent of landslides conform to flats covering very high and average risk. Moreover, petrology, slope, and, sluice slide were identified as the most effective agents in occurring land_slides. One models, Network Analysis Process (ANP) were utilized through this project                 

Dear Dariush Abolfathi, Dr Aghil Madadi, Dr Sayyad Asghari,
Volume 22, Issue 66 (10-2022)

The purpose of this study was to estimate the amount of sediment of Vanai River in Borujerd. In this research, the characteristics of the sub-basins of this river have been extracted first. These specifications include the physical characteristics of the sub-basins, including the area, the environment and length of the waterways, and the characteristics of the river flow, and its sediment content. In the following, multivariate linear regression, multilevel prefabricated neural network (MLP) and radial function-based neural network (RBF) models are used to model sediment estimation. After estimating the model, the mean square error index (RMSE) was used to compare the models and select the best model. Evidence has shown that initially the MLP's neural network model had the best estimate with the lowest error rate (90.44) and then the RBF model (151.44) among the three models. The linear regression model has the highest error rate because only linear relationships between variables are considered.

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