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Precipitation Processes01:12

Precipitation Processes

4.8K
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...
4.8K
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

4.0K
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...
4.0K
Precipitation Gravimetry01:03

Precipitation Gravimetry

13.9K
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...
13.9K
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

283
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...
283
Types of Coprecipitation01:10

Types of Coprecipitation

5.1K
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...
5.1K
Precipitation Titration: Overview01:26

Precipitation Titration: Overview

9.6K
Precipitation titration involves the reaction of a titrant and an analyte to generate an insoluble precipitate. While precipitation titration uses various precipitating agents, silver nitrate is the most common precipitating reagent; titrations involving Ag+ are called argentometric titrations. Usually, the endpoint in a precipitation titration can be detected by visual indicators.
A precipitation titration curve demonstrates the change in concentration of the titrant or analyte upon adding the...
9.6K

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Related Experiment Video

Updated: Jan 14, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.4K

Leveraging SHapley Additive exPlanations (SHAP) and fuzzy logic for efficient rainfall forecasts.

Seyed Matin Malakouti1

  • 1Amirkabir University of Technology, Tehran, Iran. matin.malakouti@aut.ac.ir.

Scientific Reports
|October 20, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a hybrid machine learning model for accurate rainfall prediction, combining Light Gradient Boosting Machine (LGBM) and fuzzy logic for rapid forecasting. The new framework offers improved accuracy and speed for meteorological services and early-warning systems.

Keywords:
Fuzzy systemLight gradient boosting machineMeteorological servicesRainfall

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Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data
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Area of Science:

  • Meteorology
  • Artificial Intelligence
  • Data Science

Background:

  • Accurate rainfall forecasting is crucial for disaster preparedness (floods, droughts) and water resource management.
  • Existing meteorological services face challenges in delivering timely and precise rainfall predictions.
  • Machine learning offers potential for enhancing weather forecasting accuracy and efficiency.

Purpose of the Study:

  • To develop and evaluate a hybrid machine learning framework for rapid and reliable rainfall forecasting.
  • To combine a Light Gradient Boosting Machine (LGBM) classifier with a fuzzy logic system for improved prediction.
  • To assess the performance of the proposed framework against conventional methods using real-world meteorological data.

Main Methods:

  • Utilized ten years of daily meteorological data from Australian locations.
  • Developed a hybrid model integrating a Light Gradient Boosting Machine (LGBM) classifier and a fuzzy logic system.
  • Performed internal validation and 10-fold cross-validation to evaluate predictive accuracy and execution time.

Main Results:

  • The LGBM model achieved 85.42% accuracy for 'rain tomorrow' and 99.6% for 'rain today'.
  • The hybrid framework demonstrated superior accuracy and computational efficiency compared to baseline algorithms.
  • The fuzzy logic component provided interpretable insights with 100% accuracy in matching validation data.

Conclusions:

  • The proposed hybrid LGBM-fuzzy logic framework offers a promising approach for accurate and fast rainfall prediction.
  • The model's interpretability enhances trust for decision-makers in applications like urban flood management and agricultural planning.
  • Further validation on diverse datasets and incorporation of additional variables are recommended for broader generalizability.