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

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...

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Autores principales:	Nishant Kumar, Kirti Soni, Ravinder Agarwal
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	atmospheric boundary layer height lstm network sodar deep learning Oceanography GC1-1581 Meteorology. Climatology QC851-999
Acceso en línea:	https://doaj.org/article/5a94aebf4c2d4e7d86befb636f722dad
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spelling	oai:doaj.org-article:5a94aebf4c2d4e7d86befb636f722dad2021-12-01T14:40:58ZPrediction of temporal atmospheric boundary layer height using long short-term memory network1600-087010.1080/16000870.2021.1926132https://doaj.org/article/5a94aebf4c2d4e7d86befb636f722dad2021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/16000870.2021.1926132https://doaj.org/toc/1600-0870Nowadays, 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.Nishant KumarKirti SoniRavinder AgarwalTaylor & Francis Grouparticleatmospheric boundary layer heightlstm networksodardeep learningOceanographyGC1-1581Meteorology. ClimatologyQC851-999ENTellus: Series A, Dynamic Meteorology and Oceanography, Vol 73, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	atmospheric boundary layer height lstm network sodar deep learning Oceanography GC1-1581 Meteorology. Climatology QC851-999
spellingShingle	atmospheric boundary layer height lstm network sodar deep learning Oceanography GC1-1581 Meteorology. Climatology QC851-999 Nishant Kumar Kirti Soni Ravinder Agarwal Prediction of temporal atmospheric boundary layer height using long short-term memory network
description	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.
format	article
author	Nishant Kumar Kirti Soni Ravinder Agarwal
author_facet	Nishant Kumar Kirti Soni Ravinder Agarwal
author_sort	Nishant Kumar
title	Prediction of temporal atmospheric boundary layer height using long short-term memory network
title_short	Prediction of temporal atmospheric boundary layer height using long short-term memory network
title_full	Prediction of temporal atmospheric boundary layer height using long short-term memory network
title_fullStr	Prediction of temporal atmospheric boundary layer height using long short-term memory network
title_full_unstemmed	Prediction of temporal atmospheric boundary layer height using long short-term memory network
title_sort	prediction of temporal atmospheric boundary layer height using long short-term memory network
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/5a94aebf4c2d4e7d86befb636f722dad
work_keys_str_mv	AT nishantkumar predictionoftemporalatmosphericboundarylayerheightusinglongshorttermmemorynetwork AT kirtisoni predictionoftemporalatmosphericboundarylayerheightusinglongshorttermmemorynetwork AT ravinderagarwal predictionoftemporalatmosphericboundarylayerheightusinglongshorttermmemorynetwork
_version_	1718404992778371072

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

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