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Related Concept Videos

Precipitation Processes01:12

Precipitation Processes

1.9K
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...
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Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

3.0K
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...
3.0K
Precipitation Gravimetry01:03

Precipitation Gravimetry

9.7K
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...
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Precipitation Titration: Endpoint Detection Methods01:19

Precipitation Titration: Endpoint Detection Methods

2.4K
In argentometric precipitation titrations, endpoints can be detected visually by the Mohr, Volhard, and Fajans methods. In the Mohr method, adding a soluble chromate indicator gives an initial yellow color to the analyte solution. As the titrant is added, the first excess of silver ions forms a red silver chromate precipitate, marking the endpoint. The solution pH should be maintained at about 8 by adding solid CaCO3.
In the Volhard method, a standard excess of AgNO3 is first added to the...
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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Related Experiment Video

Updated: Nov 5, 2025

Design and Analysis for Fall Detection System Simplification
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Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

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Multi-step rainfall forecasting using deep learning approach.

Sanam Narejo1, Muhammad Moazzam Jawaid1, Shahnawaz Talpur1

  • 1Department of Computer Systems, Mehran University of Engineering and Technology, Jamshoro, Sindh, Pakistan.

Peerj. Computer Science
|May 20, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a Temporal Deep Belief Network (DBN) for multistep rainfall prediction, outperforming Convolutional Neural Networks (CNNs). The advanced DBN model offers improved accuracy for crucial weather forecasting applications.

Keywords:
Convolutional neural networks (CNNs)Deep belief networks (DBNs)Deep learningMulti-step forecastingRainfall predictionTemporal data

Related Experiment Videos

Last Updated: Nov 5, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.9K

Area of Science:

  • Meteorology and Climatology
  • Artificial Intelligence
  • Time Series Analysis

Background:

  • Accurate rainfall prediction is vital for water resource management, flood mitigation, and hydrological modeling.
  • The inherent spatio-temporal variability and non-linear dynamics of rainfall pose significant challenges for traditional forecasting methods.
  • Advanced computational models are necessary to capture the complex processes involved in climate prediction.

Purpose of the Study:

  • To propose and evaluate a deep learning approach, specifically a Temporal Deep Belief Network (DBN), for direct multistep rainfall time series forecasting.
  • To compare the performance of the Temporal DBN against conventional models like Convolutional Neural Networks (CNNs).
  • To identify an optimal DBN architecture for rainfall prediction based on performance metrics.

Main Methods:

  • Implementation of a Temporal Deep Belief Network (DBN) for direct multistep forecasting, where each horizon uses a dedicated model.
  • Evaluation of model performance using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-value.
  • Comparison of the DBN model against baseline models, including CNNs, using observed rainfall data.

Main Results:

  • The Temporal DBN model demonstrated superior performance compared to the conventional CNN for rainfall time series forecasting.
  • A modified DBN architecture with hidden layers (300-200-100-10) achieved the best results, yielding MSE of 4.59E-05, RMSE of 0.0068, and R-value of 0.94 on testing samples.
  • Training the DBN model was found to be computationally intensive and more exhaustive than other deep learning architectures.

Conclusions:

  • The Temporal DBN is a highly effective deep learning model for accurate multistep rainfall forecasting.
  • The findings suggest that DBNs can serve as a foundation for advancing the prediction of other weather parameters under similar climatic conditions.
  • Further research into optimizing DBN training efficiency is warranted for practical implementation.