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Related Concept Videos

Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

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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...
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Precipitation Processes01:12

Precipitation Processes

596
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...
596
Precipitation Gravimetry01:03

Precipitation Gravimetry

7.6K
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...
7.6K
Types of Coprecipitation01:10

Types of Coprecipitation

882
Coprecipitation is the contamination of a precipitate by otherwise soluble species and occurs via different processes. In colloidal precipitates, coprecipitation occurs via surface adsorption. For instance, barium sulfate has a primary layer of adsorbed barium ions and a secondary layer of nitrate counterions. This results in contamination of the precipitate by barium nitrate.
Sometimes, ions in a crystal lattice can undergo isomorphous replacement by inclusions of similar charge and size. For...
882
Precipitation Titration Curve: Analysis01:21

Precipitation Titration Curve: Analysis

1.2K
The precipitation titration curve demonstrates the change in concentration of one reactant with the volume of titrant added. During the titration of chloride ions with silver nitrate, the precipitation titration curve is divided into three regions: before, at, and after the equivalence point. Before the equivalence point, low redissolution of the sparingly soluble silver chloride precipitate gives a low silver ion concentration. However, in the second region, representing the equivalence point,...
1.2K
Precipitation Titration: Endpoint Detection Methods01:19

Precipitation Titration: Endpoint Detection Methods

2.1K
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...
2.1K

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Monthly precipitation prediction based on quadratic decomposition and improved parrot algorithm.

Weijie Zhang1, Yuming Zeng1, Shubo Zhou1

  • 1School of Mechanical and Energy Engineering, Guangdong Ocean University, Yangjiang, 529500, China.

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|July 21, 2025
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Summary

This study introduces a novel CEEMDAN-TVMD-IPO-BiLSTM model for accurate regional precipitation prediction. The advanced framework significantly improves forecasting accuracy, aiding climate change adaptation and disaster management.

Keywords:
BiLSTMIPOMonthly precipitationTVMDTemporal features

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Area of Science:

  • Environmental Science
  • Data Science
  • Climate Science

Background:

  • Precipitation prediction is vital for ecological balance and economic stability.
  • The nonlinear and stochastic nature of precipitation data hinders accurate forecasting.
  • Improved regional precipitation prediction is essential for disaster prevention and climate change response.

Purpose of the Study:

  • To develop a robust and accurate model for regional precipitation prediction.
  • To enhance the prediction accuracy of complex, nonlinear precipitation time series data.
  • To provide a scientific basis for disaster mitigation and climate change adaptation policies.

Main Methods:

  • Proposed a novel precipitation prediction model: CEEMDAN-TVMD-IPO-BiLSTM.
  • Utilized CEEMDAN for initial data decomposition.
  • Employed Topology Optimization Algorithm (TTAO) to optimize Variational Mode Decomposition (VMD) for secondary decomposition.
  • Optimized Bidirectional Long Short-Term Memory (BiLSTM) using an improved Parrot Optimizer (IPO) with chaotic variations.
  • Validated the model using historical monthly precipitation data from Guangzhou, Changsha, and Emeishan in China.

Main Results:

  • The CEEMDAN-TVMD-IPO-BiLSTM model demonstrated superior performance in monthly precipitation prediction.
  • Achieved low Root Mean Square Error (RMSE) values: 32.373 (Guangzhou), 14.445 (Changsha), and 22.447 (Emeishan).
  • Attained high R-squared (R²) values: 0.960 (Guangzhou), 0.972 (Changsha), and 0.977 (Emeishan).
  • Outperformed existing models in accuracy and robustness.

Conclusions:

  • The proposed CEEMDAN-TVMD-IPO-BiLSTM model effectively predicts regional precipitation fluctuations.
  • The model offers a significant advancement in precipitation forecasting accuracy.
  • Provides valuable scientific insights for policy-making related to drought and flood management.