PREDICTING AIR POLLUTANTS BY IOT AND MACHINE LEARNING
(FOR RESEARCHERS AND ACADAMECIANS)
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we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. We experimented with a traditional model (k-nearest neighbours algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79...
we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. We experimented with a traditional model (k-nearest neighbours algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the realtime monitoring of garbage levels along with notifications from the alert mechanism.
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