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01:03Prediction Intervals
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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.
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In the absence...
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Deep transfer learning and attention based P2.5 forecasting in Delhi using a decade of winter season data.
Scientific reports·2025
Efficient multi-station air quality prediction in Delhi with wavelet and optimization-based models.
Lakshmi Sankar1, Krishnamoorthy Arasu1
1School of Computer Science Engineering, Vellore Institute of Technology, Vellore, India.
Plos One
|August 19, 2025
Summary
Accurate air quality prediction in Delhi is crucial. The AquaWave-BiLSTM model enhances forecasting using advanced feature selection and Bidirectional Long Short-Term Memory networks, improving health policy formulation.
Area of Science:
- Environmental Science
- Data Science
- Computational Science
Background:
- South Asian megacities, particularly Delhi, face severe air quality degradation.
- Elevated particulate matter (PM2.5) concentrations pose significant public health risks.
- Effective air quality prediction is vital for mitigation strategies and policy development.
Purpose of the Study:
- To introduce an innovative predictive framework, AquaWave-BiLSTM, for multi-station air quality forecasting in Delhi.
- To enhance the accuracy and efficiency of air quality prediction models.
- To provide interpretable insights into feature contributions for forecasting.
Main Methods:
- Integration of Wavelet Transform for frequency pattern extraction.
- Application of Principal Component Analysis (PCA) for dimensionality reduction.
- Hybrid Aquila Optimizer and Arithmetic Optimization (AOAOA) for feature selection.
- Utilization of a Bidirectional Long Short-Term Memory (Bi-LSTM) network for temporal analysis.
- Statistical validation using the Wilcoxon Signed-Rank Test and feature contribution analysis via SHAP.
Main Results:
- The AquaWave-BiLSTM framework achieved high predictive accuracy (MSE: 0.00065, MAE: 0.04566, RMSE: 0.02523, R²: 0.9494).
- The model demonstrated computational efficiency with an average execution time of 20.57 seconds.
- Feature selection and extraction methods were statistically validated across all monitoring stations.
Conclusions:
- AquaWave-BiLSTM offers an efficient and interpretable solution for multi-station air quality forecasting.
- The framework surpasses conventional methods in predictive accuracy.
- The approach provides valuable insights into factors influencing air quality, aiding policy decisions.