The various pre processing techniques have been used in the dataset for the implementation of machine learning models. The performance of the models have been compared for the prediction of the air quality. The results show that the Random Forest and Decision Tree based model achieves the maximum...
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 ...
Air Quality Prediction is a project that balances Arduino development and Machine Learning. I have always found the world of machine learning captivating but was never able to run models on real-time data. Arduinos provide the solution with a vast array of sensors supported on their microcontrolle...
Specifically, we develop a prediction model by combining multitask learning techniques with recurrent neural network (RNN) models and perform empirical analyses to evaluate the utility of each facet of the proposed framework based on a real-world dataset that contains 451,509 air quality records ...
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
Author statement for “Air quality prediction by machine learning models: A predictive study on the east coast of India”. Gokulan Ravindiran, Karthick Kanagarathinam, Avinash Alagumalai: writing original draft, reviewing, editing, data curation. Gasim Hayder, Christian Sonne: reviewing and editin...
In particular, we compare feed-forward neural networks, currently recognized as state-of-the-art approach for statistical prediction of air quality, with two alternative approaches derived from machine learning: pruned neural networks and lazy learning. As far as we know, LL is applied for the ...
The aim of the study, was to utilize the CNN model on the air quality dataset to detect patterns for future prediction modelling. The proposed study, is implemented on two phases where the first phase focuses on preprocessing and data analysis, whereas the Second phase is used for the ...
The prediction of air pollution is of great importance in highly populated areas because it directly impacts both the management of the city’s economic activity and the health of its inhabitants. This work evaluates and predicts the Spatio-temporal behavior of air quality in Metropolitan Lima, Per...
However, air quality prediction is a complex, systematic undertaking, and improving the accuracy of predictions is an urgent and difficult problem in the field of air pollution prevention. A goal of air quality prediction is to predict the degree of air pollution in an area for the next day,...