Urban environmental monitoring and health risk assessment introducing a fuzzy intelligent computing model
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.This study developed a novel fuzzy intelligent computing model to assess urban health risks using environmental data. The model accurately estimates risks, showing PM2.5
Area Of Science
- Environmental Health
- Intelligent Computing
- Urban Planning
Background
- Urban environments pose complex health risks.
- Evaluating these risks requires sophisticated assessment tools.
- Existing methods may lack precision in diverse conditions.
Purpose Of The Study
- Introduce a novel fuzzy intelligent computing model.
- Enhance the precision of urban health risk evaluation.
- Utilize multi-media environmental monitoring data.
Main Methods
- Employed a Fuzzy Inference System (FIS) model.
- Synthesized multi-media environmental data (PM2.5, noise, IAQI, TCI, NPI).
- Validated the model across three cities with varying pollution levels (Beijing, Kunming, Wuxi).
Main Results
- The model accurately estimated health risks (high R² values: 0.89-0.92).
- Positive correlation found between PM2.5 and health risks; noise impact varied by city.
- Model demonstrated high accuracy (low RMSE) and credibility (p < 0.05 for key indices).
Conclusions
- The fuzzy intelligent model effectively assesses urban health risks in diverse environments.
- Findings support enhanced environmental protection and health management strategies.
- Provides scientific basis for sustainable urban development and planning.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
Predicting Outbreaks
Predictive analytics, a branch of statistics, uses historical data, algorithmic models, and...
Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...