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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

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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Responses to Drought and Flooding02:41

Responses to Drought and Flooding

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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.
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

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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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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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Related Experiment Video

Updated: Oct 30, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Modular Neural Networks with Fully Convolutional Networks for Typhoon-Induced Short-Term Rainfall Predictions.

Chih-Chiang Wei1, Tzu-Heng Huang1

  • 1Department of Marine Environmental Informatics and Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung 20224, Taiwan.

Sensors (Basel, Switzerland)
|July 2, 2021
PubMed
Summary

This study uses deep learning models to forecast hourly rainfall during typhoons in Taiwan. The GRI-RRI_MCNN model significantly improved rainfall prediction accuracy, enhancing typhoon disaster preparedness.

Keywords:
convolutional networksimage segmentationpredictionrainfalltyphoon

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Area of Science:

  • Meteorology and Atmospheric Science
  • Artificial Intelligence and Machine Learning
  • Geospatial Analysis

Background:

  • Taiwan is highly susceptible to typhoons, experiencing significant natural disasters due to strong winds and heavy rainfall.
  • Accurate prediction of typhoon-induced rainfall is crucial for disaster mitigation and management.

Purpose of the Study:

  • To develop and evaluate a deep learning model for predicting hourly rainfall during typhoon events in Taiwan.
  • To compare the performance of a novel model with conventional methods for typhoon rainfall forecasting.

Main Methods:

  • Employed fully convolutional networks (FCNs), a deep learning technique for image recognition and semantic segmentation.
  • Developed two FCN models: Ground Rainfall Image-based FCN (GRI_FCN) and a combined model using radar echo and ground rainfall data (GRI-RRI_MCNN).
  • Utilized radar echo images and ground station rainfall data from southern Taiwan (2013-2019) and benchmarked against a Multilayer Perceptron (RMMLP).

Main Results:

  • The GRI-RRI_MCNN model demonstrated a comprehensive understanding of future rainfall patterns during typhoons.
  • This advanced model significantly enhanced the accuracy of hourly rainfall forecasting compared to the benchmark model.
  • Evaluated forecast horizons ranging from 1 to 6 hours, confirming improved predictive capabilities.

Conclusions:

  • The GRI-RRI_MCNN model offers a substantial advancement in predicting typhoon-related rainfall in Taiwan.
  • This improved forecasting accuracy can lead to more effective disaster preparedness and response strategies.
  • The study highlights the potential of deep learning, specifically FCNs, in meteorological forecasting for natural disaster management.