Air Quality Predictions in Urban Areas Using Hybrid ARIMA and Metaheuristic LSTM  

作　　者：S.Gunasekar G.Joselin Retna Kumar G.Pius Agbulu 

机构地区：[1]Department of Electronics and Instrumentation Engineering,SRM Institute of Science and Technology,Chennai,603203,Tamilnadu,India

出　　处：《Computer Systems Science & Engineering》2022年第12期1271-1284,共14页计算机系统科学与工程（英文）

摘　　要：Due to the development of transportation, population growth and industrial activities, air quality has become a major issue in urban areas. Poor air qualityleads to rising health issues in the human’s life in many ways especially respiratory infections, heart disease, asthma, stroke and lung cancer. The contaminatedair comprises harmful ingredients such as sulfur dioxide (SO2), nitrogen dioxide(NO2), and particulate matter of PM10, PM2.5, and an Air Quality Index (AQI).These pollutant ingredients are very harmful to human’s health and also leads todeath. So, it is necessary to develop a prediction model for air quality as regularon the basis of monthly or seasonaly. In this work, a new hybrid model for airquality prediction (AQP) is developed by using reed deer metaheuristic optimizedLong Short Term Memory (LSTM) Deep Learning network. To overcome thedrawback of the existing autoregressive integrated moving average model(ARIMA) model, the residual errors are processed by using an optimized LSTMnetwork. The red deer optimization (RDO) is a new type of metaheuristic methodwhich is motivated by the mating behaviour of Red Deer. The proposed model isbetter in terms of all prediction performance parameters when compared withother models.

关 键 词：Air quality PREDICTION ARIMA RDO 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]