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.

摘要

如今，城市的工业化，人口，人类活动，车辆交通密度，无计划的城市化以及通风不良导致的快速增长，导致大量污染物的浓度增加。大气边界层（ABL）高度是定义大城市中任何区域的污染物承载能力的基本参数。在对ABL高度进行时间序列分析和预测时，现有模型使用线性（AR，ARMA，ARIMA等）和非线性（ANN，ANFIS等）算法，但是这些模型识别隐藏模式和基础动力学的能力较弱。 ABL模式。本文提出了一个基于长期学习的算法，用于基于时间/季节和年度ABL高度预测的长期短期记忆（LSTM）模型，并确定了ABL高度模式的潜在动态。该模型的结果已与声波探测和测距（SODAR）系统进行的测量进行了比较。LSTM模型用于预测并分析受模型影响的性能。通过应用LSTM的神经网络，将观察到的ABL高度数据和模型数据用于预测ABL高度。从分析中观察到，当神经元数量等于32，一个时期等于500时，可以获得最佳结果。为了获得可接受的预测精度，已计算了各种基于错误的性能指标。已为预测值分别为17.3％和7.33％的更新网络以及观察值分别为10.62％和5.95％的更新网络计算了平均绝对百分比误差（MAPE）和相对均方根误差（rRMSE）。还，该模型的性能已被估算用于ABL高度的年度和季节预测。结果表明，rRMSE值（7.49％和5.59％）在季风后最低，对于季节预报，最高（10.29％和5.86％）在年度预报中。