Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Precipitation Processes

Precipitation Processes

The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...

Precipitation and Co-precipitation

Precipitation and Co-precipitation

Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...

Precipitation Gravimetry

Precipitation Gravimetry

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...

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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...

Rapidly Varying Flow

Rapidly Varying Flow

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...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

You might also read

Related Articles

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

Sort by

Same author

Timing-dependent renal protection of dapagliflozin in endotoxemic diabetic mice by real-time GFR and biomarkers.

Intensive care medicine experimental·2026

Same author

Activin A mitigates ferroptosis in cerebral ischemia/reperfusion injury via the PGC-1α/NRF1/TFAM axis.

Frontiers in neurology·2026

Same author

Meme-Based Packaging as Digital Cultural Translation: How Online Cultural Symbols Shape Purchase and Sharing Intentions.

Behavioral sciences (Basel, Switzerland)·2026

Same author

Generic generation and manipulation of high-dimensional spin-orbit states in Hilbert space.

Nature communications·2026

Same author

Smart double-screening system of propagating male-sterile lines for maize hybrid seed production.

Journal of integrative plant biology·2026

Same author

Unraveling the cardiovascular burden of long COVID: symptom profiles, underlying mechanisms, and clinical management insights.

Frontiers in cardiovascular medicine·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 28, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

Improved runoff forecasting based on time-varying model averaging method and deep learning.

Jinlou Ran¹, Yang Cui¹, Kai Xiang¹

¹Henan Provincial Communications Planning and Design Institute Co., Ltd, Zhengzhou, P.R. China.

|September 15, 2022

Summary

This summary is machine-generated.

This study introduces a dynamic model averaging (TV-DMA) method to enhance runoff prediction accuracy. The TV-DMA model significantly reduces prediction uncertainty compared to single models, improving forecasting stability.

More Related Videos

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

Published on: April 3, 2014

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Related Experiment Videos

Last Updated: Aug 28, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

Published on: April 3, 2014

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Area of Science:

Hydrology
Environmental Science
Data Science

Background:

Accurate runoff prediction is crucial for water resource management.
Existing models often struggle with accuracy and stability, especially during varying hydrological conditions.
The need for advanced forecasting techniques is evident.

Purpose of the Study:

To propose and validate a dynamic model averaging method with Time-varying weight (TV-DMA) for improved runoff prediction.
To construct an integrated prediction model framework using TV-DMA and deep learning.
To identify key variables influencing runoff prediction accuracy.

Main Methods:

Correlation analysis to identify significant runoff prediction variables.
Development of a Time-varying dynamic model averaging (TV-DMA) framework.
Integration of TV-DMA with deep learning algorithms for runoff forecasting.

Main Results:

Identified current/previous monthly runoff, current monthly temperature, previous monthly temperature, and current monthly rainfall as key variables.
The TV-DMA model achieved the highest prediction accuracy (0.97 Nash-efficiency coefficient - NSE).
TV-DMA reduced uncertainty bands by 33.3%-65.5% compared to single models, though performance dipped during flood seasons.

Conclusions:

TV-DMA offers superior accuracy and reduced uncertainty in runoff prediction.
Current monthly runoff, rainfall, and temperature are critical factors demanding attention in forecasting.
Model performance is sensitive to seasonal variations, with lower accuracy during flood periods.