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Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

2.1K
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...
2.1K
Precipitation Processes01:12

Precipitation Processes

608
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...
608
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
Prediction Intervals01:03

Prediction Intervals

2.3K
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. 
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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
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

105
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...
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Updated: Sep 16, 2025

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff
10:35

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

Published on: April 3, 2014

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Rainfall forecast in Brazil using machine learning.

Sidney T da Silva1, Letícia C Milani1, Enrique C Gabrick2

  • 1Department of Chemical, Federal University of Paraná, Curitiba 81531-980, PR, Brazil.

Chaos (Woodbury, N.Y.)
|July 7, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning models accurately predict rainfall across five Brazilian regions. Random forest models showed the best performance, outperforming recurrent neural networks for precipitation forecasting.

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Area of Science:

  • Environmental Science
  • Data Science
  • Climate Science

Background:

  • Machine learning (ML) is vital for predicting climate patterns and extreme weather events.
  • Accurate rainfall forecasting supports agriculture, water management, energy, and public safety.
  • ML models can anticipate climate shifts, enabling proactive planning and disaster mitigation.

Purpose of the Study:

  • To evaluate three ML models for precipitation prediction in five Brazilian regions.
  • To compare the performance of Random Forest, Long Short-Term Memory, and Bidirectional Long Short-Term Memory models.
  • To assess the effectiveness of ML in forecasting rainfall patterns using climate reanalysis data.

Main Methods:

  • Utilized Random Forest, Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) models.
  • Trained models using local temperature and Atlantic Ocean temperature as input features.
  • Used total precipitation as the target variable for prediction across five Brazilian regions.

Main Results:

  • All evaluated ML models demonstrated satisfactory performance in precipitation prediction.
  • The Random Forest model achieved lower average absolute errors compared to LSTM and BiLSTM.
  • The study confirms the efficacy of ML techniques in forecasting rainfall patterns.

Conclusions:

  • Machine learning models are effective tools for rainfall forecasting in Brazil.
  • Random Forest offers a robust and accurate approach for precipitation prediction.
  • Accurate rainfall forecasts enhance climate change adaptation and resource management strategies.