نوع مقاله : مقاله کامل علمی پژوهشی
نویسندگان
1 سبزوار-میدان طالقانی-24 متری انقلاب-انقلاب12
2 عضو هیئت علمی دانشگاه علوم کشاورزی و منابع طبیعی گرگان
3 عضو هیئت علمی دانشگاه گلستان
4 دانشگاه علوم کشاورزی و منابع طبیعی گرگان
5 تهران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Abstract1
Background and Objectives: River flooding is a natural phenomenon that can have devastating effects on human life and cause significant economic losses. Determining flood flow discharge is crucial in hydraulic and hydrological studies, as well as in the design of water structures along the river. Various approaches exist for studying river flooding and determining flow discharge, each with its own errors and limitations. Estimation of flow discharge at hydrometric stations using the stage-discharge relationship has always been one of the most prominent methods for determining river flow discharge, But the basic and important limitation of this method is that for flood discharges, it must be extrapolated from the curve, which is associated with error and uncertainty.
Also, another method such as the velocitymeters and the acoustic doppler devices, are not only risky but also very costly and time-consuming in natural river flow conditions and especially during floods. Using velocity measurement methods based on surface flow imaging is an approach that has gained attention as a non-contact method in open channel flows. In this study, a new Particle Image Velocimetry (PIV) technique using drone images has been used, which accurately, quickly, and without contact and disturbance to the water flow pattern, calculates the lateral velocity distribution and flow discharge during river floods.
Materials and Methods: To achieve the research objectives, drone filming was conducted at the Ziarat River section at its confluence with the Qara-Tappeh River during the May 2023 flood. In this way, the imaging of the river flow surface at a height of 40 meters was performed by a UAV vertically for 40 seconds at a frequency of 30 frames per second using a drone. After processing the images in PIVLAB software within the MATLAB environment, the surface velocity of the flow in the cross section of the river was calculated and finally the lateral velocity distribution and river flow discharge under flood conditions were calculated using the velocity-area method and compared with the measured data.
Results: In this study, the velocity index (k) was used to measure the discharge and convert the surface velocity to the average velocity of the section using image-based tracking methods. With the help of this method, the lateral velocity distribution and flow discharge in the river under flood conditions without contact with the flow in a section was investigated using two algorithms defined in PIVLAB, the results of which show high accuracy in estimating the flow discharge and the lateral velocity distribution in the studied section with this method. so that the flow discharge was estimated and calculated with an error of about 3.5% and the average cross-sectional velocity distribution with an error of about 7%.
Conclusion: Based on the results obtained and considering that accurate measurement of river flood flow in a short time is of particular importance, processing drone images to estimate river flow and also the lateral distribution of river velocity can be an effective measure in this regard.
Abstract1
Background and Objectives: River flooding is a natural phenomenon that can have devastating effects on human life and cause significant economic losses. Determining flood flow discharge is crucial in hydraulic and hydrological studies, as well as in the design of water structures along the river. Various approaches exist for studying river flooding and determining flow discharge, each with its own errors and limitations. Estimation of flow discharge at hydrometric stations using the stage-discharge relationship has always been one of the most prominent methods for determining river flow discharge, But the basic and important limitation of this method is that for flood discharges, it must be extrapolated from the curve, which is associated with error and uncertainty.
Also, another method such as the velocitymeters and the acoustic doppler devices, are not only risky but also very costly and time-consuming in natural river flow conditions and especially during floods. Using velocity measurement methods based on surface flow imaging is an approach that has gained attention as a non-contact method in open channel flows. In this study, a new Particle Image Velocimetry (PIV) technique using drone images has been used, which accurately, quickly, and without contact and disturbance to the water flow pattern, calculates the lateral velocity distribution and flow discharge during river floods.
Materials and Methods: To achieve the research objectives, drone filming was conducted at the Ziarat River section at its confluence with the Qara-Tappeh River during the May 2023 flood. In this way, the imaging of the river flow surface at a height of 40 meters was performed by a UAV vertically for 40 seconds at a frequency of 30 frames per second using a drone. After processing the images in PIVLAB software within the MATLAB environment, the surface velocity of the flow in the cross section of the river was calculated and finally the lateral velocity distribution and river flow discharge under flood conditions were calculated using the velocity-area method and compared with the measured data.
Results: In this study, the velocity index (k) was used to measure the discharge and convert the surface velocity to the average velocity of the section using image-based tracking methods. With the help of this method, the lateral velocity distribution and flow discharge in the river under flood conditions without contact with the flow in a section was investigated using two algorithms defined in PIVLAB, the results of which show high accuracy in estimating the flow discharge and the lateral velocity distribution in the studied section with this method. so that the flow discharge was estimated and calculated with an error of about 3.5% and the average cross-sectional velocity distribution with an error of about 7%.
Conclusion: Based on the results obtained and considering that accurate measurement of river flood flow in a short time is of particular importance, processing drone images to estimate river flow and also the lateral distribution of river velocity can be an effective measure in this regard.
کلیدواژهها [English]
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