Air Quality Predictions in Urban Areas Using Hybrid ARIMA and Metaheuristic LSTM
نویسندگان
چکیده
Due to the development of transportation, population growth and industrial activities, air quality has become a major issue in urban areas. Poor leads rising health issues human’s life many ways especially respiratory infections, heart disease, asthma, stroke lung cancer. The contaminated comprises harmful ingredients such as sulfur dioxide (SO2), nitrogen (NO2), particulate matter PM10, PM2.5, an Air Quality Index (AQI). These pollutant are very also death. So, it is necessary develop prediction model for regular on basis monthly or seasonaly. In this work, new hybrid (AQP) developed by using reed deer metaheuristic optimized Long Short Term Memory (LSTM) Deep Learning network. To overcome drawback existing autoregressive integrated moving average (ARIMA) model, residual errors processed LSTM red optimization (RDO) type method which motivated mating behaviour Red Deer. proposed better terms all performance parameters when compared with other models.
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ژورنال
عنوان ژورنال: Computer systems science and engineering
سال: 2022
ISSN: ['0267-6192']
DOI: https://doi.org/10.32604/csse.2022.024303