Air quality predictionAir quality index (AQI)Hybrid optimizationFeature selectionBPSO-BWAO-RFAir pollution poses a significant threat to public health and environmental sustainability, necessitating accurate predictive models for effective air quality management. This study uses machine learning techniques to ...
As mentioned above, the ultimate goal of AQI prediction using the ADNNet is to minimize the prediction error by finding a nonlinear function F. We use the learning algorithm A to map a finite air pollution dataset DU into the function F and minimize the loss function L(DU;F;Θ). The le...
Air Quality Index Prediction ADNNet: Attention-based deep neural network for Air Quality Index prediction Introduction This repository provides the implementation of ADNNet for AQI prediction. The experiments have been performed datasets: AQI Online Testing and Analysis Platform. If you use this repo,...
Urban activities, particularly vehicle traffic, are contributing significantly to environmental pollution with detrimental effects on public health. The ability to anticipate air quality in advance is critical for public authorities and the general publi
To improve the accuracy of the air quality index prediction algorithm, certain columns were removed from the dataset. The wind direction and station columns were excluded based on their limited impact on the prediction, as determined by domain knowledge and previous research findings. Additionally, th...
While Machine Learning has achieved some results in air quality decision analysis and prediction, the advent of Deep Learning has brought Machine Learning closer to AI. Deep Learning can train Deep Neural Networks, extract features, and transform, abstract, and process information. Thus, it has adv...
By simulating spatiotemporal dynamics of air mass and encouraging the adherence to the physical laws of mass conservation and continuity for all samples, including prediction points and those beyond the observation dataset, the trained model exhibits robust accuracy in spatiotemporal extrapolation (low ge...
(EMD), a transformer and a bidirectional long short-term memory neural network (BiLSTM), which is good at addressing the ultrashort-term prediction of nonlinear time-series data and shows good performance for application to the air quality dataset of Patna, India (6:00 am on October 3, ...
As a result, the prediction employed the normal dataset with a correlation threshold of 0.5 When the value was greater than 0.5, the connection was quite positive. The correlation coefficient between AQI and PM2.5, AQI and PM10 was obtained as 0.94. Similarly 0.58 for NO2, 0.57 for CO, ...
Dataset Loaders AddRemove Hamid-Nasiri/Recurrent-Fuzzy-Neural-Network 66 Tasks Time Series Forecasting Time Series Prediction Similar Datasets Box-Jenkins Usage Created with Highcharts 9.3.0Number of Papers20222024202120232025010.250.50.751.25Air Quality IndexLorenz DatasetBox-Jenkins ...