Exploring a deep LSTM neural network to forecast daily PM 2.5 concentration using meteorological parameters in Kathmandu
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Exploring a deep LSTM neural network to forecast daily PM2.5 concentration using meteorological parameters in Kathmandu Valley, Nepal Sandeep Dhakal 1
&
Yogesh Gautam 1 & Aayush Bhattarai 1
Received: 9 June 2020 / Accepted: 17 August 2020 # Springer Nature B.V. 2020
Abstract Fine particulate matter (PM2.5) is a complex air pollutant with numerous gaseous and solid constituents. PM2.5 possesses a significant hazard due to its ability to penetrate deep into the lungs, corrode the alveolar wall, and impair lung functions. Modeling the non-linear and dynamic time series of daily PM2.5 concentration remains a challenge. This study proposes a deep LSTM neural network to forecast accurate PM2.5 concentration in the Kathmandu valley. Correlation analysis illustrates that dew, minimum ambient temperature, maximum ambient temperature, and pressure are strongly correlated with PM2.5 concentration. Hence, five models are developed based on different input parameter combinations and are eventually evaluated to determine the best performing model. Model 2 with single-step prediction is the best performing deep LSTM model with RMSE of 13.04 μg/ m3 and MAE of 10.81 μg/m3. The SARIMA model applied to the univariate PM2.5 data series illustrates the RMSE of 19.54 μg/ m3 and MAE of 15.21 μg/m3 for the test data. Hence, the deep LSTM model with past PM2.5 data and dew as inputs is recommended to predict future PM2.5 concentration in the Kathmandu valley. The negative impact of PM2.5 concentration on public health can be minimized with efficient forecasting. Keywords PM2.5 . Long short-term memory . LSTM . SARIMA model . Correlation analysis
Introduction Rapid urbanization and industrialization have imparted a more serious challenge of environmental pollution to the modern world (Liang et al. 2019). Air pollution draws particular attention among other challenges. Air pollutants are dispersed in the atmosphere with moving air masses, polluting not only the regions of direct emission but also the encompassing environment. Air pollution is a complex mixture of many air pollutants and the United States Environmental Protection Agency (EPA) regulates six crucial ones based on human health and environmental criteria (Environmental Protection Agency 2020). These six include airborne particulate matter (PM), carbon monoxide (CO), nitrogen dioxides (NO2), sulfur dioxides (SO2), ground-level ozone (O3), and lead (Pb). Air pollution is currently the fourth fatal health risk worldwide
* Sandeep Dhakal [email protected] 1
Department of Mechanical and Aerospace Engineering, Institute of Engineering, Pulchowk Campus, Kathmandu 44700, Nepal
surpassing metabolic risks, dietary risks, and tobacco smoking (The World Bank and Institute for Health Metrics and Evaluation 2016). One in ten deaths is attributed to air pollution worldwide (The World Bank 2016) with 7 million premature deaths annually (World Health Organization 2014). Quite a few epidemiological researches have illustrated the direct impact of PM on human health (Dockery and Pope
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