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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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Precipitation Processes01:12

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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 Gravimetry01:03

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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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Precipitation Titration Curve: Analysis01:21

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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,...
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Types of Coprecipitation01:10

Types of Coprecipitation

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

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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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Navigating Samarinda's climate: A comparative analysis of rainfall forecasting models.

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  • 1Department of Physics, Faculty of Mathematics and Natural Science, Mulawarman University.

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Summary
This summary is machine-generated.

This study compared traditional and machine learning models for rainfall prediction in Samarinda City. The Neural Network model demonstrated superior accuracy, aiding in hydrometeorological disaster mitigation.

Keywords:
ARIMAExponential smoothingForecastingNeural networkTime series modelingTraditional and Machine Learning Models in Forecasting: Exponential Smoothing, ARIMA, NN

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Area of Science:

  • Environmental Science
  • Data Science
  • Meteorology

Background:

  • Accurate rainfall data modeling is crucial for mitigating weather-related natural disasters.
  • Samarinda City faces risks from hydrometeorological events, necessitating reliable forecasting.
  • Existing methods require evaluation against advanced techniques for improved prediction.

Purpose of the Study:

  • To compare the predictive accuracy of traditional (Exponential Smoothing, ARIMA) and machine learning (Neural Network) models for monthly rainfall in Samarinda City.
  • To identify the most effective model for rainfall forecasting to support disaster mitigation efforts.
  • To analyze rainfall trends and inform the development of early warning systems.

Main Methods:

  • Utilized monthly rainfall data from Samarinda City (2000-2020) provided by the Meteorology, Climatology, and Geophysics Agency.
  • Implemented Exponential Smoothing, ARIMA, and a Neural Network (Backpropagation with data standardization) model.
  • Evaluated model performance using Root Mean Squared Error Prediction (RMSEP) on a 90:10 training-testing split.

Main Results:

  • The Neural Network model exhibited higher accuracy in predicting rainfall compared to Exponential Smoothing and ARIMA.
  • Rainfall forecasting indicated a trend of highest precipitation occurring between November and March.
  • The study identified specific months with a high likelihood of rainfall, crucial for disaster preparedness.

Conclusions:

  • The Neural Network model is recommended for accurate rainfall prediction in Samarinda City.
  • Predicted high rainfall periods can be leveraged for early warnings of floods and landslides.
  • Findings support the development of disaster mitigation policies, including water discharge management, based on rainfall predictions.