Comparative Analysis Of Machine Learning Techniques For Predicting Air

Comparative Analysis Of Machine Learning Techniques For Predicting Air In this paper, we have performed pollution prediction using four advanced regression techniques and present a comparative study to determine the best model for accurately predicting air quality with reference to data size and processing time. In this paper, we have performed the pollution prediction using four advanced regression techniques and have presented a comparative study to analyze the best model for accurately.

Pdf A Comparative Study On Air Quality Analysis And Prediction Using Urban air quality is steadily declining, affecting not only the air itself but also impacting the quality of water and land. this paper explores the utilization of machine learning based algorithms for analysis and prediction of air quality in smart cities. In this paper, we have performed pollution prediction using four advanced regression techniques and present a comparative study to determine the best model for accurately predicting air quality with reference to data size and processing time. Recent advancements in deep learning have enhanced prediction capabilities by automatically extracting features and managing complex data. this paper compares machine learning and deep learning approaches in air quality forecasting, highlighting their strengths and weaknesses. They applied different machine learning methods called svm, gvm, ann, and autoregressive non linear neural networks to predict air pollution. the comparative findings claimed that the narx method with refined data gave the most accurate results in predicting pm2.5 and pm10 concentrations.

Pdf Comparative Analysis Of Machine Learning Algorithms On Different Recent advancements in deep learning have enhanced prediction capabilities by automatically extracting features and managing complex data. this paper compares machine learning and deep learning approaches in air quality forecasting, highlighting their strengths and weaknesses. They applied different machine learning methods called svm, gvm, ann, and autoregressive non linear neural networks to predict air pollution. the comparative findings claimed that the narx method with refined data gave the most accurate results in predicting pm2.5 and pm10 concentrations. In this paper, we performed pollution forecasting using machine learning techniques while presenting a comparative study to determine the best model to accurately predict air quality. Comparative analysis of machine learning models for prediction of air quality index published in: 2024 international conference on intelligent systems and advanced applications (icisaa). Abstract in several urban industrial regions affected by air pollution, it is crucial to monitor air quality in order to improve the quality of life and prevent any damage to health. this paper mainly focuses on the prediction of air quality index (aqi) using two different machine learning algorithms svm and knn. Traditional models (arima, sarima, and exponential smoothing) and machine learning techniques (svm, lstm, and prophet) are employed to forecast aqi over time. r squared, root mean squared error and mean absolute error were then used to compare the prediction performance of each model.