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Related Concept Videos

Precipitation Processes01:12

Precipitation Processes

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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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Precipitation and Co-precipitation01:17

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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 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.
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Updated: Mar 12, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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Ensemble and temporal feature-based framework for rainfall classification in Bangladesh.

Mahir Shahriar Tamim1, Md Samiul Alim1, Tanvir Ahmed Khan1

  • 1Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh.

Plos One
|March 10, 2026
PubMed
Summary

This study developed a machine learning framework for accurate rainfall intensity classification in Bangladesh, crucial for agriculture and disaster management. Random Forest achieved the highest accuracy, identifying humidity and sunshine as key predictors.

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Area of Science:

  • Meteorology and Climatology
  • Data Science and Machine Learning

Background:

  • Monsoon variability in Bangladesh significantly impacts agriculture, water resources, and disaster preparedness.
  • Accurate daily rainfall classification is vital for effective management strategies.

Purpose of the Study:

  • To develop and evaluate a robust machine learning framework for classifying daily rainfall intensity across Bangladesh.
  • To identify key meteorological predictors influencing rainfall intensity.

Main Methods:

  • Utilized over 543,839 daily weather records from 35 meteorological stations.
  • Compared various machine learning (Random Forest, XGBoost, LightGBM, CatBoost) and deep learning (ANN, DNN, 1D-CNN, LSTM, Bi-LSTM) models.
  • Employed class weighting to handle data imbalance and LIME/SHAP for model interpretability.

Main Results:

  • Random Forest achieved the highest classification accuracy at 77.37%.
  • Bidirectional LSTM (Bi-LSTM) performed best among deep learning models with 76.97% accuracy.
  • Humidity and sunshine duration were identified as the most influential predictors, with significant interactions between lagged humidity and temperature.

Conclusions:

  • The developed machine learning framework provides reliable rainfall intensity classification for Bangladesh.
  • Findings support data-driven agricultural planning, early flood warnings, and climate-resilient disaster management.