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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

25
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...
25
Typical Model Studies01:30

Typical Model Studies

155
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.
155
Rapidly Varying Flow01:24

Rapidly Varying Flow

28
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...
28
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

79
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.
79
Survival Tree01:19

Survival Tree

39
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
39

You might also read

Related Articles

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

Sort by
Same author

Crop-weed classification using deep learning: a comparative study of CNNs, vision transformers, and interpretable models.

Scientific reports·2026
Same author

Decoding common and rare noncoding variant effects across cellular and developmental contexts.

Nature genetics·2026
Same author

Vascular smooth muscle cell state trajectories mediate molecular mechanisms of coronary disease risk.

Nature communications·2026
Same author

Advancements in MRI Conditionality of Spinal Cord Stimulation Systems: A Narrative Review of Recent SCS Systems and Their Associated Risks in MRI Operations.

Pain physician·2025
Same author

Assessing compound flood drivers in Peninsular India: Multivariate copula-based approach.

Journal of environmental management·2025
Same author

Interactions Between Dietary Metabolites and Regulatory Risk Variants for Human Colon Cancer.

bioRxiv : the preprint server for biology·2025
Same journal

Mercury exposure in wintering Cinereous vultures (Aegypius monachus) on the Korean Peninsula: tissue and feather distribution and associations with brain cholinesterase activity.

Environmental monitoring and assessment·2026
Same journal

Modulation of fine aerosol ionic profiles and chemical characteristics by crop residue burning over the North-Western Indo-Gangetic Plain.

Environmental monitoring and assessment·2026
Same journal

Green citrate sol-gel synthesis of CuMn₂O₄/Dy₂O₃ nanocomposite for efficient separation, preconcentration, and determination of Cd(II) in food and environmental samples.

Environmental monitoring and assessment·2026
Same journal

Risk elements contamination in the riverbed sediments of the Xiangjiang River, China: a review.

Environmental monitoring and assessment·2026
Same journal

Effects of the built environment on the quantity, quality, and ecological functions of dissolved organic matter and nutrients in the residential stormwater ponds (Florida, USA).

Environmental monitoring and assessment·2026
Same journal

Development and application of a Schiff-base colorimetric sensor for lead (Pb<sup>2+</sup>) detection in borehole water from Kaduna metropolis, Nigeria.

Environmental monitoring and assessment·2026
See all related articles

Related Experiment Video

Updated: May 10, 2025

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

8.9K

Bayesian-optimized recursive machine learning for predicting human-induced changes in suspended sediment transport.

Soumya Kundu1, Somil Swarnkar2, Akshay Agarwal3

  • 1Department of Earth and Environmental Sciences, IISER Bhopal, Madhya Pradesh, Bhopal, Pin - 462066, India.

Environmental Monitoring and Assessment
|April 26, 2025
PubMed
Summary
This summary is machine-generated.

Human activities like dam construction have significantly reduced river sediment load, impacting water resources and ecosystems. Machine learning models, particularly extra trees regressor, show high accuracy in predicting suspended sediment load.

Keywords:
Human interventionMachine learningRecursive prediction; Bayesian optimizationSuspended sediment loadTree-based models

More Related Videos

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.1K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

7.9K

Related Experiment Videos

Last Updated: May 10, 2025

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

8.9K
Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.1K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

7.9K

Area of Science:

  • Environmental Science
  • Hydrology
  • Water Resource Management
  • Machine Learning Applications

Background:

  • Suspended sediment load (SSL) is a critical indicator for river health, morphology, and water resource management.
  • Anthropogenic factors, including dam construction and land-use changes, significantly influence riverine sediment dynamics.
  • Historical data analysis is essential for understanding long-term trends in SSL and their drivers.

Purpose of the Study:

  • To analyze historical changes in suspended sediment load (SSL) in the Godavari River Basin.
  • To evaluate the effectiveness of machine learning (ML) models for predicting SSL.
  • To understand the impact of anthropogenic activities on sediment transport patterns.

Main Methods:

  • Historical SSL data (1969-2020) divided into pre-1990 and post-1990 periods.
  • Statistical analysis of SSL trends, seasonal distribution, and transport patterns using empirical cumulative distribution functions (ECDF).
  • Development and evaluation of tree-based ML models (Extra Trees Regressor, Random Forest Regressor, Gradient Boosting Regressor) using R², RMSE, and MAE metrics.

Main Results:

  • A significant decline in mean annual SSL was observed post-1990 (from 136.85 to 62.38 million tons) attributed to human interventions.
  • While seasonal SSL distribution remained consistent (~73% during monsoon), median and peak SSL values decreased, indicating reduced sediment availability.
  • The Extra Trees Regressor (ETR) model achieved the highest prediction accuracy (R²=0.97 training, 0.9 testing), outperforming other ML models.

Conclusions:

  • Human modifications have substantially altered sediment transport dynamics in the Godavari River Basin.
  • Ensemble tree-based ML models, especially ETR, provide a robust and accurate approach for SSL prediction.
  • Findings offer crucial insights for effective river basin management and sustainable sediment modeling under evolving hydrological conditions.