Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Typical Model Studies01:30

Typical Model Studies

646
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
646
Modeling and Similitude01:12

Modeling and Similitude

677
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
677
Rapidly Varying Flow01:24

Rapidly Varying Flow

544
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
544
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

790
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
790
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

360
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
360
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

290
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
290

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Dataset of Water Stable Isotopes in Iran's Main Aquifers.

Scientific data·2025
Same author

Groundwater quality assessment in upper Kabul basin and Paghman aquifer.

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering·2024
Same author

Application of RiTiCE in understanding hydro-meteorological controls on ice break-up patterns in River Tornionjoki.

Environmental monitoring and assessment·2024
Same author

The combined effects of anthropogenic and climate change on river flow alterations in the Southern Caspian Sea Iran.

Heliyon·2024
Same author

Assessing wildfire impact on Trigonella elliptica habitat using random forest modeling.

Journal of environmental management·2024
Same author

Nordic socio-recreational ecosystem services in a hydropeaked river.

The Science of the total environment·2023

Related Experiment Video

Updated: Feb 21, 2026

Image-based Lagrangian Particle Tracking in Bed-load Experiments
10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

9.5K

River suspended sediment modelling using the CART model: A comparative study of machine learning techniques.

Bahram Choubin1, Hamid Darabi1, Omid Rahmati2

  • 1Department of Watershed Management, Sari Agriculture Science and Natural Resources University, P.O. Box 737, Sari, Iran.

The Science of the Total Environment
|October 6, 2017
PubMed
Summary

The Classification and Regression Tree (CART) model effectively estimates suspended sediment load (SSL) in rivers using hydro-meteorological data. This CART model outperformed other common methods, offering a valuable tool for water resource management.

Keywords:
Adaptive neuro-fuzzy inference systemClassification and regression treesHaraz watershedMulti-layer perceptron neural networkSupport vector machineSuspended sediment load

More Related Videos

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.9K
Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
09:49

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Published on: November 18, 2015

12.9K

Related Experiment Videos

Last Updated: Feb 21, 2026

Image-based Lagrangian Particle Tracking in Bed-load Experiments
10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

9.5K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.9K
Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
09:49

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Published on: November 18, 2015

12.9K

Area of Science:

  • Environmental Science
  • Hydrology
  • Water Resources Management

Background:

  • Suspended sediment load (SSL) is crucial for water quality and aquatic habitats, making its accurate modeling essential for environmental management.
  • While Classification and Regression Tree (CART) algorithms are used in ecological and geomorphological studies, their application to riverine SSL estimation remains unexplored.

Purpose of the Study:

  • To evaluate the efficacy of a CART model for estimating riverine SSL using hydro-meteorological data.
  • To compare the performance of the CART model against established time series models like ANFIS, MLP, RBF-SVM, and P-SVM for SSL prediction.

Main Methods:

  • Utilized hydro-meteorological data (river discharge, stage, rainfall) and monthly SSL data from the Kareh-Sang River, Iran.
  • Explored various input data combinations and time lags to optimize SSL estimation.
  • Employed statistical metrics including Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE), and percent bias (PBIAS) for model evaluation.

Main Results:

  • The CART model demonstrated superior performance in predicting SSL, achieving an NSE of 0.77 and KGE of 0.8, with PBIAS within ±15%.
  • The RBF-SVM model showed the second-best performance with NSE=0.68, KGE=0.72, and PBIAS within ±15%.
  • Optimal input data combinations were identified using trial and error, Taylor diagrams, and violin plots.

Conclusions:

  • The CART model is a highly effective tool for suspended sediment load estimation in river basins with available hydro-meteorological data.
  • The findings support the integration of CART modeling into environmental and water resource management strategies for improved sediment transport analysis.