Prediction of temporal atmospheric boundary layer height using long short-term memory network

Abstract

Nowadays, the city’s rapid growth of industrialisation, population, human activities, vehicular traffic density, unplanned urbanisation with poor ventilation contributes to increasing large amount of pollutants concentration. Atmospheric Boundary Layer (ABL) height is a basic parameter to define the pollution carrying capacity of any area in a big city. In the time series analysis and prediction of ABL height, the existing models use linear (AR, ARMA, ARIMA etc.) and non-linear (ANN, ANFIS etc) algorithms, but these models less capable of identifying the hidden pattern and underlying dynamics of ABL patterns. This paper presents a Long Short-Term Memory (LSTM) model using deep learning-based algorithms for temporal/seasonal and annual ABL height prediction and identified the latent dynamics of the ABL height pattern. The results of the model have been compared with the measurements made by SOnic Detection And Ranging (SODAR) system. LSTM model is used for prediction and to analyse their performance affected by the model. The observed ABL height data and model data are used to predict the ABL height by applying the neural network of LSTM. It is observed from the analysis that the optimal results can be achieved when the number of neurons is equal to 32, an epoch is equal to 500. To obtain the acceptable accuracy of prediction, various error-based performance indices have been calculated. Mean Absolute Percentage Error (MAPE) and relative Root Mean Square Error (rRMSE) have been calculated for the updated network with predicted values 17.3% and 7.33%, and, for the updated network with observed values 10.62% and 5.95%, respectively. Also, the performance of the proposed model has been estimated for the annual and seasonal prediction of ABL height. The results depict rRMSE values (7.49% and 5.59%) as lowest during post-monsoon for seasonal prediction and (10.29% and 5.86%) highest for annual prediction.