Study on Various Machine Learning Techniques for Pollution Forecasting - Chaudhary, Ifshita; Sharma, Shruti; Sethi, Preeti (International Journal of Computer Sciences and Engineering, 2019)
Because of a significant increment of pollution in the air, it is required to foresee the pollution of the following dates, months and years. Air pollution is quickly expanding because of different human factors and reasons, such as the generation of synthetic compounds, particulates, pollutants or in-organic materials and other substances which is even the reason for the loss of human lives and even additionally hurts the indigenous habitat like plants and animals, etc. Undoubtedly, air pollution is one of the significant natural problems in metropolitan and urban areas. In this way, Monitoring and safeguarding air quality is one of the most fundamental exercises in numerous modern and urban territories today. Consequently, air quality assessment, observing, and forecast has turned into significant research. The point of this paper is to explore different Machine Learning based strategies especially artificial neural network models for air quality determining in various conditions. This scheme for the future will elaborate on the distributed research results identifying with air quality index and forecast utilizing techniques predicting air quality of a particular area using Neural Networks. Therefore, as of now under this scheme, we will derive the comparative analyses of various neural network Algorithms from past researchers i.e. ANN, MLP, CNN, LSTM, CNN-LSTM, Encoder decoder, and Convolution LSTM. To find the efficiency and effectiveness in the area of air contamination and pollution.