Bhareh gharedaghy; amir ghasemzadeh
Abstract
Due to its high environmental diversity, Iran has a high rank in crises caused by natural disasters. Flood as one of the natural disasters, following the rapid growth of cities and climate changes in many regions, has caused severe social and economic, health and environmental damage. For this reason, ...
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Due to its high environmental diversity, Iran has a high rank in crises caused by natural disasters. Flood as one of the natural disasters, following the rapid growth of cities and climate changes in many regions, has caused severe social and economic, health and environmental damage. For this reason, predict of flood susceptibility is so essential that failure to identify flood susceptibility may increase its destructive effects. Recently, with the advancement of remote sensing tools, geographic information, machine learning and statistical models, it is possible to create a more accurate flood susceptibility map. For this purpose, in this research, by using Sentinel satellite images and using the Ensemble approach with six machine learning models, flood susceptibility was predicted in the Karun watershed. Individual models include Generalized Linear Model (GLM), Boosted Regression Tree (BRT), Support Vector Machine (SVM), Random Forest (RF), Multivariate Adaptive Regression Splines (MARS), Maximum Entropy (MAXENT). The results of this study show that the northeast of Aligudarz city, parts of Durud and Azna in Lorestan province, Khademmirza, Shahrekord and Kiyar in Chaharmahal Bakhtiari province, Dana and Boyer Ahmad in Kohkiloye and Boyer Ahmad province, Semirom city in Isfahan province and the southern border areas of Karun River in Khuzestan province has the highest flood potential in this basin. The results of this research are effective for managers and planners and will prevent development in vulnerable areas and reduce financial and economic losses in the future.
yazdan yarahmadi; hojatolah younesi; Ahmad Godarzi; saeed rostami
Abstract
Determining the value of the runoff coefficient is one of the biggest problems and the main source of uncertainty in many water resources projects. The aim of the current research is to estimate the runoff coefficient by combining Arc CN-Runoff, SCS-CN and ICAR experimental relationship in the Selseleh ...
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Determining the value of the runoff coefficient is one of the biggest problems and the main source of uncertainty in many water resources projects. The aim of the current research is to estimate the runoff coefficient by combining Arc CN-Runoff, SCS-CN and ICAR experimental relationship in the Selseleh watershed. Selseleh Study area is located in the north of Lorestan province and is one of the sub-watersheds of Kashkan. In order to carry out this research, data including digital elevation model information, land use classes, soil texture and meteorological and hydrological statistics (rainfall and runoff) related to the research area were used. The map of the land use layer and soil hydrological groups was entered into the Arc CN-Runoff tool environment, and Intersect was applied on two layers the Land soil layer was prepared, and the runoff coefficient map was prepared based on this layer. Finally, the runoff coefficient was estimated in three conditions dry, medium and wet moisture conditions and a comparison was made. The results of the research showed that the runoff coefficient (CR) in the Selseleh Study area is 0.26, 0.53, and 0.77, respectively, in dry, medium, and wet conditions. Therefore, the dry humid state has decreased by 68% compared to the average, and the more humid state has increased by 37% compared to the average. Investigating the correlation between the runoff coefficient and watershed characteristics showed that the runoff coefficient is influenced by the six physiographic features of the Study area: area, slope, length of the waterway and Gravelius coefficient, the maximum height and density of the waterway, which has a significant effect on the value of the runoff coefficient in this Study area. The value of the runoff coefficient in the ICAR method for the entire Study area was 0.48. In the Selseleh Study area, the runoff coefficient has a decreasing trend from April to September. The type of land and soil use in the basin under study is one of the influential factors that affect the runoff coefficient and, consequently, the peak discharge of the Study area. Also, in early spring, the flood potential is high in the said Study area. Among the measures to increase water infiltration are the establishment of a rainwater collection system and the operation of the Farrow meter along with the increase of plant cover with seeding and planting and intercropping of pasture plants.
