Air quality prediction using stacked bi- long short-term memory and convolutional neural network in India

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Air quality is a critical aspect of environmental health, and its assessment and prediction serve as pivotal components in mitigating the adverse effects of air pollution. This study focuses on advancing air quality prediction in India through the application of cutting-edge deep learning techniques, specifically the Stacked Bidirectional Long Short-Term Memory (Bi-LSTM) and Convolutional Neural Network (CNN) architecture. Through meticulous preprocessing - encompassing missing value handling, normalization, and temporal sequencing - the dataset is prepared for the Stacked Bi-LSTM and CNN hybrid model. The model architecture leverages the temporal sequence-capturing capabilities of Stacked Bi-LSTM layers, enhancing it with the spatial feature extraction prowess of CNN layers. This integrated approach aims to address the intricate and nonlinear dependencies present in air quality time series data. During the training phase, the Adam optimizer is used to fine-tune the model’s hyperparameters, with Mean Squared Error (MSE) serving as the loss function. Important assessment metrics, including as Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and MSE, are used to evaluate the performance of the model. Furthermore, this study conducts a detailed temporal analysis, unraveling diurnal, seasonal, and long-term trends in air quality fluctuations. The study aims to offer valuable insights into the temporal and spatial patterns of air quality in India, thereby aiding environmental policymakers, urban planners, and researchers in formulating effective strategies for air quality management. The application of Stacked Bi-LSTM and CNN architectures in this research holds promise for enhancing real-time forecasting accuracy and facilitating informed decision-making towards sustainable environmental practices.