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

Design Example: Design of an Irrigation Channel01:27

Design Example: Design of an Irrigation Channel

301
Trapezoidal channels are widely used in irrigation systems due to their cost-effectiveness and efficiency in conveying water. Trapezoidal channels feature a flat bottom and sloping sides, making them stable and easier to construct compared to other shapes. The bottom width and side slope ratio are determined based on the required flow capacity and site conditions. The side slope is kept gentle for unlined channels to prevent soil erosion.Hydraulic parameters in channel design include the flow...
301
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

153
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
153
Design Example: Maintaining Level of an Embankment01:19

Design Example: Maintaining Level of an Embankment

181
Constructing a roadway embankment over uneven terrain requires precise leveling to ensure stability and proper drainage. Surveyors use a leveling instrument and staff to calculate ground elevations and determine the required fill material at each point along the embankment alignment.The process begins by positioning a leveling instrument near a benchmark with a known elevation. A backsight reading establishes the instrument height, which serves as a reference for subsequent measurements. A...
181
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

383
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.
383
Typical Model Studies01:30

Typical Model Studies

487
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.
487
Modeling and Similitude01:12

Modeling and Similitude

378
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...
378

You might also read

Related Articles

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

Sort by
Same author

Organ-specific carcinogenic risk heterogeneity of perfluorooctanoic acid exposure: Global evidence from bibliometric mapping, meta-analysis, and nonlinear dose-response modeling.

Journal of hazardous materials·2026
Same author

Effectiveness of a Structured Pharmacist-led Intervention in Improving Pediatric Outpatient Prescribing Quality in a Resource-limited Setting: A Pre-post Study.

Journal of research in pharmacy practice·2026
Same author

Predictive validity of the simplified Radiographic Assessment of Lung Edema score for mortality in critically ill patients with COVID-19.

Frontiers in medicine·2026
Same author

Prediction of In-Hospital Respiratory Support Among Children Aged 2-59 Months Hospitalized with Pneumonia in Southern Vietnam: A Retrospective Cohort Study.

Journal of clinical medicine·2026
Same author

Association of Serum 25-Hydroxyvitamin D and Vitamin D Receptor Gene Variants With Dengue Severity in Vietnamese Children.

Sage open pediatrics·2026
Same author

Correction: Costache et al. Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors. <i>Sensors</i> 2021, <i>21</i>, 280.

Sensors (Basel, Switzerland)·2026

Related Experiment Video

Updated: Oct 17, 2025

Mesocosm-Scale Constructed Wetland Design for Wastewater Treatment
08:24

Mesocosm-Scale Constructed Wetland Design for Wastewater Treatment

Published on: May 2, 2025

453

Developing a new approach for design support of subsurface constructed wetland using machine learning algorithms.

Xuan Cuong Nguyen1, Thi Thanh Huyen Nguyen1, Quyet V Le2

  • 1Laboratory of Energy and Environmental Science, Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam; Faculty of Environmental Chemical Engineering, Duy Tan University, Da Nang, 550000, Viet Nam.

Journal of Environmental Management
|October 10, 2021
PubMed
Summary

Machine learning models accurately predict subsurface constructed wetland (SCW) effluent quality. The Cubist algorithm offers the best performance for designing SCWs that meet environmental standards.

Keywords:
AmmoniumConstructed wetlandDesignMachine learningOrganic matter

More Related Videos

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.1K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.2K

Related Experiment Videos

Last Updated: Oct 17, 2025

Mesocosm-Scale Constructed Wetland Design for Wastewater Treatment
08:24

Mesocosm-Scale Constructed Wetland Design for Wastewater Treatment

Published on: May 2, 2025

453
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.1K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.2K

Area of Science:

  • Environmental Engineering
  • Water Treatment Technologies
  • Machine Learning Applications

Background:

  • Predicting effluent quality is crucial for designing compliant treatment systems.
  • Subsurface constructed wetlands (SCWs) are a key technology for wastewater treatment.
  • Optimizing SCW design requires accurate predictive tools.

Purpose of the Study:

  • To develop machine learning-based predictive models for subsurface constructed wetland (SCW) design.
  • To identify the optimal machine learning algorithm for predicting SCW effluent quality.
  • To provide a tool for designing SCWs that meet environmental standards.

Main Methods:

  • Compiled 618 data sets from SCW literature (2009-2020) with 10 features.
  • Compared five machine learning algorithms: Random Forest, CART, SVM, KNN, and Cubist.
  • Utilized influent concentrations, C:N ratio, hydraulic loading rate, and physical parameters as input features.

Main Results:

  • All nine input features were confirmed as relevant for predictive modeling.
  • The Cubist algorithm demonstrated superior performance with the lowest RMSE for NH4-N and COD.
  • Cubist achieved high coefficients of determination (0.92 for NH4-N, 0.93 for COD) and explained 84% of effluent variance.

Conclusions:

  • Machine learning, particularly the Cubist algorithm, provides a robust approach for predicting SCW effluent quality.
  • The developed Cubist-based model can effectively aid in the design of SCWs for environmental compliance.
  • A practical Cubist algorithm-based design tool for SCWs has been proposed.