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AN ALGORITHMIC APPROACH FOR POLLUTION MONITORING
This work presents the detailed analysis for predicting the cause of pollution by using Support Vector Machine (SVM), Random forest algorithm, Naive Bayes and K-nearest neighbors (KNN) algorithm.
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AN ALGORITHMIC APPROACH FOR POLLUTION MONITORING
This work presents the detailed analysis for predicting the cause of pollution by using Support Vector Machine (SVM), Random forest algorithm, Naive Bayes and K-nearest neighbors (KNN) algorithm.
The problem AN ALGORITHMIC APPROACH FOR POLLUTION MONITORING solves
Air pollution monitoring and controlling in an urban area, was carried that aids an intelligent pollution prediction and visualisation. Pollution level is observed by kriging interpolated field. These data are further done with recurrent neural network for long memories. An alarming threshold is set for prediction data from server in future. The relation among air and noise pollution by spatiotemporal approach was investigated. These values are updated for forecasting and in earth map under real time environment. A detailed study was carried for variation in factors like crowd, traffic change, movement of vehicle and intrusions from industries.
Challenges we ran into
Error in code, Malfunction of sensors, Difficulties in implementing cloud.
Discussion