Related Concept Videos
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
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01:22Multi-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.
In the absence...
In the absence...
106
01:07End Point Prediction: Gran Plot
332
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
For potentiometric titration, the Gran plot is created by plotting...
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Deep learning based multimodal urban air quality prediction and traffic analytics.
Saad Hameed1, Ashadul Islam1, Kashif Ahmad2
1Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Scientific Reports
|December 13, 2023
Summary
Accurate air quality forecasting is now possible using a novel AI framework that integrates environmental sensor data and traffic density. This system improves predictions for urban pollution, aiding public health and environmental management.
Area of Science:
- Environmental Science
- Artificial Intelligence
- Public Health
Background:
- Urban activities, especially vehicle traffic, significantly contribute to environmental pollution and negatively impact public health.
- Accurate air quality prediction is crucial for authorities and the public to manage urban activities and mitigate adverse effects.
- Advancements in AI and sensor technology enable sophisticated air quality forecasting by integrating diverse environmental factors.
Purpose of the Study:
- To present a novel, multi-modal framework for air quality prediction integrating environmental sensor data and traffic density.
- To address data inconsistencies from sensor/camera malfunctions and real-world complexities in streaming datasets.
- To develop a system capable of predicting air quality in areas with limited or failed sensor coverage.
Main Methods:
- Integration of real-time data from environmental sensors and traffic density extracted from Closed Circuit Television (CCTV) footage.
- Development of a multi-modal framework to handle data inconsistencies, noise, and outliers in streaming datasets.
- Utilizing a Particle Swarm Optimization (PSO)-based merit fusion for training joint models on data from nearby sensors to predict air quality at unmonitored locations.
Main Results:
- The proposed framework effectively integrates diverse data sources and addresses real-world data complexities.
- Evaluated using Long Short-Term Memory (LSTM) variants (Bi-directional LSTM, CNN-LSTM, ConvLSTM), the system demonstrated significant prediction improvements.
- Achieved percentage improvements over the ARIMA model: 48% (short-term), 67% (medium-term), and 173% (long-term).
Conclusions:
- The novel AI-driven framework offers a robust solution for accurate air quality prediction in urban environments.
- The system's ability to handle data inconsistencies and predict for unmonitored areas enhances its practical applicability.
- The significant improvements achieved by LSTM variants highlight the potential of advanced AI in environmental monitoring and public health protection.