Enriched Spatial-Temporal Sequence in Improved Sparse Auto Encoder with Deep Learning for Air Quality Prediction

  • P. Shree Nandhini, Dr. P. Amudha, Dr. S. Sivakumari

Abstract

Accurate monitoring of air quality is of great importance to daily human life. Through the minimize life threats in the non-linear data and feature extraction in the Timely multidimensional warnings of the process. Air quality prediction plays an essential role in the process. Predicting air quality called Improved Sparse Autoencoder with Deep Learning (ISAE-DL) was developed with diverse neural networks, improved sparse network and Long-Short-Term Memory (LSTM) for retrieving Spatio-temporal relations better prediction of air quality process. In ISAE-DL, the spatially and temporally similar locations were collected by applying k-Nearest Neighbor-Dynamic Time Wrapping Distance (kNN-DTWD) method.kNN-DTWD selects the exact nominee locations but neglects time-consuming delay. The consuming time delay long-term delay is essential for long-term predictions. So in this paper, concentric circle-based clustering is introduced to cluster the similar candidate locations considering long time delay. In concentric circle-based clustering, Manhattan distance is used to group the spatially and temporally similar locations. Initially, the locations are split into four regions using its centre—the circle-based cluster clusters the locations effectively for even long time delay based locations.  The results are merged and transferred to ISAE for air quality prediction. Concentric circle-based clustering and terrain information is processed along with the Particulate Matter (PM) and meteorological data to Artificial Neural Network (ANN), LSTM, and Convolutional Neural Network (CNN). Thus, the proposed Enriched spatial-temporal sequence ISAE-DL (EISAE-DL) improves the prediction accuracy by considering the long time delay based on locations. The experimental results show the effectiveness of the proposed EISAE-DL in terms of accuracy, precision, sensitivity, specificity, Area Under Curve (AUC), and Matthew's correlation coefficient (MCC).

Published
2021-09-10
How to Cite
P. Shree Nandhini, Dr. P. Amudha, Dr. S. Sivakumari. (2021). Enriched Spatial-Temporal Sequence in Improved Sparse Auto Encoder with Deep Learning for Air Quality Prediction. Design Engineering, 11314 - 11326. Retrieved from http://www.thedesignengineering.com/index.php/DE/article/view/4220
Section
Articles