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Related Concept Videos

Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

1.9K
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

567
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...
567
Precipitation Gravimetry01:03

Precipitation Gravimetry

7.0K
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...
7.0K
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
Types of Coprecipitation01:10

Types of Coprecipitation

815
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...
815
Precipitation Reactions03:10

Precipitation Reactions

51.3K
In a precipitation reaction, aqueous solutions of soluble salts react to give an insoluble ionic compound – the precipitate. The reaction occurs when oppositely charged ions in solution overcome their attraction for water and bind to each other, forming a precipitate that separates out from the solution. Since such reactions involve the exchange of ions between ionic compounds in aqueous solution, they are also referred to as double displacement, double replacement, exchange reactions, or...
51.3K

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MEEMD Decomposition-Prediction-Reconstruction Model of Precipitation Time Series.

Yongtao Wang1,2, Jian Liu1, Rong Li1

  • 1State Key Laboratory of Advanced Design and Manufacture for Vehicle Body, Hunan University, Lushan South Road, Yuelu District, Changsha 410082, China.

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|September 9, 2022
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Summary

This study introduces an improved precipitation prediction model (MDPRM) that enhances accuracy by decomposing time series data and using advanced algorithms. The new model significantly reduces errors and improves consistency for better water resource management and drought mitigation.

Keywords:
convolutional neural network (CNN)improved overall mean empirical modality (MEEMD)improved overall mean empirical modality decomposition–prediction–reconstruction model (MDPRM)particle swarm optimization support vector machine (PSO-SVM)recurrent neural network (RNN)

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Area of Science:

  • Hydrology and Climate Science
  • Artificial Intelligence and Machine Learning
  • Time Series Analysis

Background:

  • Accurate precipitation forecasting is crucial for water resource management and disaster preparedness.
  • Traditional models often struggle with the non-stationary nature of precipitation time series data, leading to prediction inaccuracies.
  • Existing methods require improvement to enhance the reliability of precipitation predictions.

Purpose of the Study:

  • To develop an improved overall mean empirical modal decomposition-prediction-reconstruction model (MDPRM) for accurate precipitation time series prediction.
  • To enhance the prediction accuracy of precipitation data by addressing the limitations of existing models.
  • To provide a robust tool for water resource allocation, scheduling, and drought mitigation.

Main Methods:

  • Decomposition of non-stationary precipitation time series using improved overall mean empirical modal decomposition (MEEMD).
  • Prediction of decomposed terms using a hybrid approach combining particle swarm optimization support vector machine (PSO-SVM), convolutional neural network (CNN), and recurrent neural network (RNN).
  • Reconstruction of final predictions by superimposing the results from individual decomposition terms.

Main Results:

  • The proposed MDPRM significantly reduced the Mean Absolute Percentage Error (MAPE) from 0.31 to 0.09.
  • Root Mean Square Error (RMSE) was reduced from 0.56 to 0.30, and the consistency index (α) improved from 0.33 to 0.86.
  • The model accurately predicted future precipitation trends and indicated normal agricultural drought levels for the Wujiang River basin.

Conclusions:

  • The MDPRM demonstrates superior prediction accuracy compared to traditional methods like BP, SVM, PSO-SVM, CNN, and RNN.
  • The model offers valuable technical support for regional water resource management, scheduling, and effective drought mitigation strategies.
  • Accurate precipitation forecasting using advanced AI techniques is essential for addressing climate change impacts and ensuring water security.