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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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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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Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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What is Weather?01:07

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Precipitation Gravimetry01:03

Precipitation Gravimetry

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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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What is Climate?01:16

What is Climate?

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Climate refers to the prevailing weather conditions in a specific area over an extended period. As the saying goes, “Climate is what you expect. Weather is what you get.” Climate is influenced by geographic factors, such as latitude, terrain, and proximity to bodies of water.
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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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Improving multiple model ensemble predictions of daily precipitation and temperature through machine learning

Dinu Maria Jose1, Amala Mary Vincent2, Gowdagere Siddaramaiah Dwarakish3

  • 1Department of Water Resources and Ocean Engineering, National Institute of Technology Karnataka, Surathkal, Mangaluru, India. dinumariajose@gmail.com.

Scientific Reports
|March 19, 2022
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Summary

This study shows that machine learning models, particularly Long Short-Term Memory (LSTM) and Random Forest (RF), significantly improve climate model simulations for precipitation and temperature over an Indian basin compared to traditional methods.

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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Area of Science:

  • Climate Science
  • Machine Learning Applications
  • Hydrological Modeling

Background:

  • General Circulation Models (GCMs) are crucial for climate simulations but often require improvement.
  • Multi-Model Ensembles (MMEs) are a common technique to enhance GCM performance.
  • Evaluating various MME techniques is essential for accurate climate projections.

Purpose of the Study:

  • To assess the efficacy of different MME techniques for precipitation and temperature.
  • To compare traditional averaging methods with advanced machine learning approaches.
  • To identify optimal MME strategies for a tropical Indian river basin.

Main Methods:

  • Utilized 21 GCMs from NASA NEX-GDDP and 13 GCMs from CMIP6 datasets.
  • Applied arithmetic mean, Multiple Linear Regression (MLR), Support Vector Machine (SVM), Extra Tree Regressor (ETR), Random Forest (RF), and Long Short-Term Memory (LSTM) for ensembling.
  • Evaluated MME performance using coefficient of determination (R²).

Main Results:

  • LSTM demonstrated superior performance for precipitation MMEs, achieving an R² of 0.9.
  • All machine learning methods outperformed the arithmetic mean ensemble.
  • RF and LSTM showed consistent high performance for temperature MMEs, with R² values from 0.82 to 0.93.

Conclusions:

  • Recommends RF and LSTM methods for developing MMEs in the studied basin.
  • Machine learning approaches offer significant advantages over the mean ensemble method for climate data.
  • Advanced MME techniques are vital for improving the accuracy of climate projections.