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Precipitation Processes01:12

Precipitation Processes

574
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...
574
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

2.0K
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...
2.0K
Precipitation Gravimetry01:03

Precipitation Gravimetry

7.0K
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...
7.0K
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.7K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.7K
Responses to Drought and Flooding02:41

Responses to Drought and Flooding

10.9K
Water plays a significant role in the life cycle of plants. However, insufficient or excess of water can be detrimental and pose a serious threat to plants.
10.9K
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

95
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...
95

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Related Experiment Video

Updated: Sep 3, 2025

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
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Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

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Modelling monthly pan evaporation utilising Random Forest and deep learning algorithms.

Mustafa Abed1, Monzur Alam Imteaz1, Ali Najah Ahmed2

  • 1Department of Civil and Construction Engineering, Swinburne University of Technology, Hawthorn, Melbourne, VIC, 3122, Australia.

Scientific Reports
|July 30, 2022
PubMed
Summary

This study demonstrates that advanced machine learning models, including convolutional neural networks (CNN), can accurately estimate monthly pan evaporation (Ep) rates. These AI techniques significantly outperform traditional empirical methods for water loss prediction.

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A High Performance Impedance-based Platform for Evaporation Rate Detection
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Area of Science:

  • Hydrology
  • Environmental Science
  • Artificial Intelligence

Background:

  • Evaporation is a critical component of the hydrological cycle, driving significant water loss.
  • Accurate measurement of evaporation is essential for water resource management.
  • Evaporation is a complex, nonlinear process influenced by various climatic factors.

Purpose of the Study:

  • To evaluate the feasibility of using Random Forest (RF), Convolutional Neural Network (CNN), and Deep Neural Network (DNN) for estimating monthly pan evaporation (Ep).
  • To compare the performance of machine learning models against established empirical methods.

Main Methods:

  • Utilized monthly weather data from four Malaysian stations (2000-2019).
  • Investigated various input attributes to identify optimal predictor variables for machine learning models.
  • Benchmarked RF, CNN, and DNN against Stephens & Stewart and Thornthwaite empirical methods.

Main Results:

  • All three machine learning models (RF, CNN, DNN) demonstrated superior performance over empirical methods.
  • The CNN model exhibited the highest accuracy in estimating monthly Ep, outperforming other ML frameworks.
  • The developed CNN model effectively captured the non-linear nature of evaporation.

Conclusions:

  • Machine learning, particularly CNN, offers a more precise approach to estimating monthly pan evaporation compared to traditional methods.
  • The CNN framework is a viable and effective tool for modeling monthly water loss due to evaporation.
  • Accurate evaporation prediction using AI can enhance hydrological cycle monitoring and water resource management.