Hydrogeomorphology
ahmad godarzi; hojatolah younesi; babak shahinejad; hassan torabi
Abstract
In rivers, sediment load monitoring is mainly confined to suspended load measurement; as a result, maximizing resources and reducing the damage caused by river flow is critical. The goal of this study was to use Mike3D.2018 software to create a three-dimensional simulation of the Kashkan river flow in ...
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In rivers, sediment load monitoring is mainly confined to suspended load measurement; as a result, maximizing resources and reducing the damage caused by river flow is critical. The goal of this study was to use Mike3D.2018 software to create a three-dimensional simulation of the Kashkan river flow in the spring of 2019. For this objective, HEC-RAS5.0.7 software is introduced and input according to the production of altitude cultivar (coming from mapping) from the bed and floodplain of the analyzed river with a length of 1200 meters and a scale of 1: 1000 for numerical modeling. Flood, suspended sediment, and transition sediment were estimated using data from the Kashkan-Poldakhtar hydrometric station for return periods of 25, 200, 1000, and 1250 years. Floods were highest at 1200 and 1100 cross sections and lowest at 50 and 350 cross sections, according to the model's findings. By comparing the values with the observed values, it was discovered that the simulation at Kashkan-Poldakhtar hydrometric station performed better. Total sediment simulated 207.45 million tons per day and suspended load utilizing Young’s relation with + 11.87 percent error. The amount of transitional suspended sediments in April (5132779/31) was also higher than in January (9890/55), February (41083/73), March (149629/75), and May (15/112617), according to the findings. In addition, compared to the typical silt in the Kashkan River, the amount of sediment in this month is quite large.
Geomorphology
hojatolah younesi; ahmad godarzi; Masoud Shakarami
Abstract
Today, hybrid models of artificial intelligence are considered as a suitable method for simulating hydrological phenomena, including quantitative estimation of river flow. For this purpose, there are various approaches in hydrology to estimate the flow rate of rivers, of which artificial intelligence ...
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Today, hybrid models of artificial intelligence are considered as a suitable method for simulating hydrological phenomena, including quantitative estimation of river flow. For this purpose, there are various approaches in hydrology to estimate the flow rate of rivers, of which artificial intelligence models are the most important. Therefore, in this study, the performance of support vector-wavelet regression, backup vector-gray wolf regression and bat-support vector regression models to simulate the flow of Kashkan river located in Lorestan province during the statistical period of 2010-2011 in the daily time scale were analyzed. The criteria of correlation coefficient, root mean square error and mean absolute value of error and bias were selected for evaluation and performance of the models. The results showed that the hybrid models have acceptable results in simulating the river discharge. Comparison of models also showed that the support-wavelet vector regression model in the validation stage showed values of R2 = 0.960, RMSE = 0.045, MAE = 0.024, NS = 0.968 and BIAS = 0.001 in predicting daily river flow. . Overall, the results showed that the use of hybrid support-wavelet regression model can be useful in predicting daily discharge.
Geomorphology
Saeid Roustami; Babak Shahinejad; Hojatolah Younesi; Hassan Torabipoudeh; Reza Dehghani
Abstract
Flood is one of the natural phenomena that causes a lot of human and financial losses in the world every year and creates many problems for the economic and social development of countries. Therefore, in order to reduce the damage, control and guidance of this phenomenon, estimating flood discharge and ...
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Flood is one of the natural phenomena that causes a lot of human and financial losses in the world every year and creates many problems for the economic and social development of countries. Therefore, in order to reduce the damage, control and guidance of this phenomenon, estimating flood discharge and identifying the factors affecting it is very important. In this study, in order to estimate the flood discharge of Kashkan catchment located in Lorestan province, new hybrid artificial intelligence models including artificial neural network - innovative gunner, artificial neural network - black widow spider and artificial neural network - chicken crowding during the period 1300-1400 were used. To evaluate the simulation performance, statistical indices of determination coefficient (R2), absolute mean error (MAE), Nash-Sutcliffe productivity coefficient (NSE), bias percentage (PBIAS) were used. The results showed that hybrid artificial intelligence models improve the performance of the single model. The results showed that the artificial neural network- innovative gunner model has more accuracy and less error than other models. Overall, the results showed that the use of hybrid artificial intelligence models is effective in estimating flood discharge and can be considered as a suitable and rapid solution in water resources management.
hojatolah younesi; ahmad godarzi; behzad javadi
Abstract
1-IntroductionFlood is a natural phenomenon that human societies have accepted as an unavoidable event caused by a number of factors, depending on the climatic and natural conditions of the region. It is believed that the relationship between rainfall and runoff is significantly different from one basin ...
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1-IntroductionFlood is a natural phenomenon that human societies have accepted as an unavoidable event caused by a number of factors, depending on the climatic and natural conditions of the region. It is believed that the relationship between rainfall and runoff is significantly different from one basin to another. Given that, in order to prevent the occurrence of such harmful phenomena, it is not possible at present to make changes in the factors and elements of the atmosphere. Therefore, any principled and useful solution should be sought on the ground, especially in watersheds. From this point of view, areas with "high potential" for flood production should be identified. Accordingly, the first measure to reduce the risk of floods for the sustainable settlement of the population is to control floods at their source, namely, the watershed sub-basins. Thus, it is essential to identify floodplains within the basin; however, due to the large size and scope of the watersheds, is not possible to carry out modeling, implementation and remediation operations throughout the basin. Thus, it is advised to use various computerized models to prevent floods.2-MethodologyIn this study, it has been attempted to combine GIS and multi-criteria decision-making systems (MCDM) to identify areas with different degrees of flood risk for sustainable settlement of the population in each of the cities of Khorasan Razavi Province, Iran. For this purpose, first the data of 6 effective parameters including Maximum discharge with 2, 3, 5, 10, 25, 50, 100 and 200 year return periods obtained from HEC-HMS software output, drainage density, land use and vegetation, CN, slope and permeability of the study area were prepared in GIS software environment. Then, using ANP method and pairwise comparison, the weight of each criterion and the weight of the classes of each layer were calculated in Super Decision software, respectively. Then, using GIS software analysis functions, the whole range was zoned for each of the specified criteria. Ultimately, through combination of the zoned maps and based on the weights of the ANP, the final map was prepared in five classes of low-risk flooding and high-risk flooding areas.3-Results and DiscussionThe results area of the cities exposed to floods with a very high degree as well as flood risk zoning with a return period of 2 years in the entire Khorasan Razavi province show more than 86% of areas with low and very low flooding, 12.2% of medium areas and 1.8% with high. While the results of flood zoning in the 200-year return period show 41.3% low flooding, 31.4% moderate flooding, 13.3% high flooding and 14.1% very high flooding in the entire province.4-ConclusionsThe results of this study were analyzed using field visits and ground control, which indicates that all selection criteria are met revealing the usefulness of combining MCDM methods with GIS in identifying areas with different degrees of flood risk.Keywords: Flood risk, Population settlement, Couple comparison, Khorasan Razavi
