VayuAnukulani: Adaptive Memory Networks for Air Pollution Forecasting Air pollution is the leading environmental health hazard globally due to various sources which include factory emissions, car exhaust, and cooking stoves. As a precautionary measure, air pollution forecast serves as the basis for taking effective pollution control measures, and accurate air pollution forecasting has become an important task. In this paper, we forecast finegrained ambient air quality information for 5 prominent locations in Delhi based on the historical and realtime ambient air quality and meteorological data reported by Central Pollution Control board. We present VayuAnukulani system, a novel endtoend solution to predict air quality for next 24 hours by estimating the concentration and level of different air pollutants including nitrogen dioxide (NO 2 ), particulate matter (PM 2.5  and PM 10 ) for Delhi. Extensive experiments on data sources obtained in Delhi demonstrate that the proposed adaptive attention based Bidirectional LSTM Network outperforms several baselines for classification and regression models. The accuracy of the proposed adaptive system is 15xe2x80x9320% better than the same offline trained model. We compare the proposed methodology on several competing baselines, and show that the network outperforms conventional methods by 7xe2x80x9318%.1

Predicting concentration levels of air pollutants by transfer learning and recurrent neural network Abstract   Air pollution (AP) poses a great threat to human health, and people are paying more attention than ever to its prediction.xc2xa0Accurate prediction of AP helps people to plan for theirxc2xa0outdoor activities and aids protecting human health. In this paper, longshort term memory (LSTM) recurrent neural networks (RNNs) have been used toxc2xa0predict the future concentration of air pollutants (APS) in Macau. Additionally, meteorological data and data on the concentration of APS have been utilized. Moreover, in Macau, some air qualityxc2xa0monitoring stations (AQMSs) have less observed data in quantity,xc2xa0and, at the same time, some AQMSs recorded less observed data of certain types of APS. Therefore, the transfer learning and pretrained neuralxc2xa0networks have been employed to assist AQMSs with less observed data toxc2xa0build a neural network with high prediction accuracy. The experimental sample covers a period longer than 12year and includes daily measurements from several APS as well as other more classical meteorological values. Records from five stations, four out of them are AQMSs and the remaining one is an automatic weather station, have been prepared from the aforesaid period and eventually underwent to computational intelligence techniques to build and extract a prediction knowledgebased system. As shownxc2xa0by experimentation, LSTM RNNs initialized with transfer learning methods have higher prediction accuracy; it incurred shorterxc2xa0training time than randomly initialized recurrent neural networks.