Skip to main content

River discharge measurements

A simplified method for estimating the alpha coefficient in surface velocity based river discharge measurements


Remote sensing techniques for river monitoring facilitate faster measurement campaigns compared to traditional methods, reduce risks to personnel and instruments, and allow measurements under critical flow conditions. An alpha coefficient (α) is commonly employed to convert surface velocities, obtained by contactless techniques, into depth-averaged velocities, which are used for the application of the velocity-area method for assessing discharge. Some optical-based software programs use a constant α value, based on a theoretical “standard”. However, analyses of empirical vertical velocity profiles in real cases reveal that α can significantly deviate from this standard due to various factors (roughness, turbulence, etc.).

This study analyzes several ADCP (Acoustic Doppler Current Profiler)-based measurements in Sicily, Italy, to explore factors influencing flow velocity distribution and potential errors from using the standard α for discharge estimation via surface velocity-based methods. The results confirmed substantial variability in α, which is functionally related to some geometric factors characterizing the cross-section shape and the specific vertical where the velocity profile is computed. The generated dataset of empirical α values is also used to implement an Artificial Neural Network (ANN), offering a straightforward tool suitable for non-contact techniques. 

The ANN predicts α at any vertical of a measurement transect as a function of variables however necessary for discharge assessment by non-intrusive methods, leading to depth-averaged velocity estimates from surface velocities that are more accurate than those derived from conventional approaches, as demonstrated by four test cases.This study underscores the importance of local alpha coefficients over a constant global value in improving discharge estimation accuracy. Using simplified approaches, such the ANN here proposed, this is possible also without using expensive and not always usable instrumentations, such the ADCP. Although the analysis covered a wide range of cases, only small rivers with predominantly torrential and, in some cases, ephemeral flow regimes were considered. The roughness conditions examined are those that predominantly characterize Sicilian rivers, and the ADCP measurements were conducted prevalently under ordinary flow conditions.

Thus, while the findings align with other studies on different river types, they should be contextualized exclusively to rivers and conditions similar to those explored here. The same considerations should be extended to the suitability of the proposed ANN. Given the growing interest in contactless river monitoring methods, conducting similar analyses on other rives types would be highly beneficial to validate and expand the applicability of these results and the proposed ANN. This represents a promising direction for future research.

Velocity, acceleration, speed, momentum, displacement, vector, scalar, uniform motion, non-uniform motion, relative velocity, instantaneous velocity, average velocity, terminal velocity, angular velocity, tangential velocity, linear velocity, escape velocity, velocity-time graph, projectile motion, kinematics

#velocity, #speed, #momentum, #acceleration, #displacement, #vector, #scalar, #motion, #relativevelocity, #instantaneousvelocity, #averagevelocity, #terminalvelocity, #angularvelocity, #tangentialvelocity, #linearvelocity, #escapevelocity, #velocitytimegraph, #projectilemotion, #kinematics

International Conference on Network Science and Graph Analytics

Visit: networkscience.researchw.com

Award Nomination: networkscience-conferences.researchw.com/award-nomination/?ecategory=Awards&rcategory=Awardee

For Enquiries: support@researchw.com

Get Connected Here
---------------------------------
---------------------------------
instagram.com/network_science_awards
tumblr.com/emileyvaruni
n.pinterest.com/network_science_awards
networkscienceawards.blogspot.com
youtube.com/@network_science_awards

Comments

Post a Comment

Popular posts from this blog

HealthAIoT: Revolutionizing Smart Healthcare! HealthAIoT combines Artificial Intelligence and the Internet of Things to transform healthcare through real-time monitoring, predictive analytics, and personalized treatment. It enables smarter diagnostics, remote patient care, and proactive health management, enhancing efficiency and outcomes while reducing costs. HealthAIoT is the future of connected, intelligent, and patient-centric healthcare systems. What is HealthAIoT? HealthAIoT is the convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) in the healthcare industry. It integrates smart devices, sensors, and wearables with AI-powered software to monitor, diagnose, and manage health conditions in real-time. This fusion is enabling a new era of smart, connected, and intelligent healthcare systems . Key Components IoT Devices in Healthcare Wearables (e.g., smartwatches, fitness trackers) Medical devices (e.g., glucose monitors, heart rate sensors) Rem...
Post a Comment
Read more
Detecting Co-Resident Attacks in 5G Clouds! Detecting co-resident attacks in 5G clouds involves identifying malicious activities where attackers share physical cloud resources with victims to steal data or disrupt services. Techniques like machine learning, behavioral analysis, and resource monitoring help detect unusual patterns, ensuring stronger security and privacy in 5G cloud environments. Detecting Co-Resident Attacks in 5G Clouds In a 5G cloud environment, many different users (including businesses and individuals) share the same physical infrastructure through virtualization technologies like Virtual Machines (VMs) and containers. Co-resident attacks occur when a malicious user manages to place their VM or container on the same physical server as a target. Once co-residency is achieved, attackers can exploit shared resources like CPU caches, memory buses, or network interfaces to gather sensitive information or launch denial-of-service (DoS) attacks. Why are Co-Resident Attack...
Post a Comment
Read more
                        Neural Networks Neural networks are computing systems inspired by the human brain, consisting of layers of interconnected nodes (neurons). They process data by learning patterns from input, enabling tasks like image recognition, language translation, and decision-making. Neural networks power many AI applications by adjusting internal weights through training with large datasets.                                                    Structure of a Neural Network Input Layer : This is where the network receives data. Each neuron in this layer represents a feature in the dataset (e.g., pixels in an image or values in a spreadsheet). Hidden Layers : These layers sit between the input and output layers. They perform calculations and learn patterns. The more hidden layers a ne...
Post a Comment
Read more