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

Gradually Varying Flow01:29

Gradually Varying Flow

34
Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
34
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

127
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.
127
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

280
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
280
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

40
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...
40
Precipitation Gravimetry01:03

Precipitation Gravimetry

5.4K
Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
In determining nickel by gravimetric analysis, a precipitant of ethanolic dimethylglyoxime is added to a hot nickel salt solution. This is quickly followed by the dropwise addition of dilute ammonia solution until precipitation occurs. A...
5.4K
Rapidly Varying Flow01:24

Rapidly Varying Flow

51
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...
51

You might also read

Related Articles

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

Sort by
Same author

Analyzing climate change trends and projection of their effects on wood equilibrium moisture content using CMIP6 models under SSP scenarios in Iran.

Scientific reports·2026
Same author

Hydrogeochemical, Isotopic, and Modeling Insights Into Groundwater Salinization in a Coastal Semiarid Aquifer System.

Water environment research : a research publication of the Water Environment Federation·2026
Same author

Emerging Invasive Weeds in Iran: Occurrence, Ecological Impacts, and Sustainable Management.

Plants (Basel, Switzerland)·2025
Same author

Mechanics of knee meniscus results from precise balance between material microstructure and synovial fluid viscosity.

PloS one·2025
Same author

Anomaly detection in groundwater monitoring data using LSTM-Autoencoder neural networks.

Environmental monitoring and assessment·2024
Same author

Innovative approach for predicting daily reference evapotranspiration using improved shallow and deep learning models in a coastal region: A comparative study.

Journal of environmental management·2024

Related Experiment Video

Updated: Jun 7, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.0K

Enhancing streamflow prediction in a mountainous watershed using a convolutional neural network with gridded data.

Zahra Hajibagheri1, Mohammad Mahdi Rajabi2,3, Ebrahim Asadi Oskouei4

  • 1Civil and Environmental Engineering Faculty, Tarbiat Modares University, Tehran, Iran.

Environmental Science and Pollution Research International
|November 9, 2024
PubMed
Summary

This study shows a convolutional neural network (CNN) model effectively simulates daily streamflow using image-based environmental data from ERA5-Land. The model accurately predicts lower flow rates, demonstrating a cost-efficient approach for hydrological forecasting.

Keywords:
Deep neural networkERA5-Land datasetForward feature selectionHydrological modelingStreamflow simulation

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

471
Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.1K

Related Experiment Videos

Last Updated: Jun 7, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.0K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

471
Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.1K

Area of Science:

  • Hydrology
  • Environmental Science
  • Machine Learning

Background:

  • Accurate streamflow simulation is crucial for water resource management and flood prediction.
  • Traditional hydrological models often require extensive calibration and may struggle with complex spatial variability.
  • The integration of remote sensing data and advanced machine learning offers a promising avenue for improved hydrological modeling.

Purpose of the Study:

  • To demonstrate the effectiveness of a convolutional neural network (CNN) model for daily streamflow simulation in a mountainous watershed.
  • To evaluate the utility of image-based environmental variables from the ERA5-Land dataset for streamflow prediction.
  • To optimize the CNN model using feature selection for enhanced efficiency and accuracy.

Main Methods:

  • Utilized a convolutional neural network (CNN) architecture tailored for streamflow prediction.
  • Employed image-based inputs derived from the ERA5-Land dataset, including temperature, snowmelt, soil water content, and precipitation.
  • Applied forward feature selection (FFS) to identify key predictive variables and optimize model complexity.

Main Results:

  • The CNN model achieved high accuracy in simulating daily streamflow, validated by metrics like RMSE, MAPE, R2, and NSE.
  • The model demonstrated enhanced accuracy in predicting lower streamflow rates, particularly during autumn and winter (RMSE of 2.02 m³/s for flows < 13.8 m³/s).
  • Forward feature selection identified total evaporation and volumetric soil water as crucial parameters, leading to a more efficient model with comparable accuracy.

Conclusions:

  • An image-based approach using CNN models is a practical and effective method for streamflow prediction.
  • The ERA5-Land dataset provides a valuable, cost-efficient, and accessible data source for hydrological modeling.
  • Optimized CNN models offer a robust tool for understanding and predicting watershed hydrology, especially for low-flow conditions.