Wyszukiwarka
Abstrakt
A reliable air quality prediction model is required for pollution control, human health monitoring, and sustainability. The existing air quality prediction models lack efficiency due to overfitting in prediction model and local optima trap in feature selection. This study proposes the Balanced Spider Monkey Optimization (BSMO) technique for effective feature selection to overcome the local optima trap and overfitting problems. The air quality prediction data were collected from the Central Pollution Control Board (CPCB) from four cities in India: Bangalore, Chennai, Hyderabad, and Cochin. Normalization is performed using Min-Max Normalization and fills the missing values in the dataset. A Convolutional Neural Network (CNN) is applied to provide deep representation of the input dataset. The BSMO technique selects the relevant features based on the balancing factor and provides the relevant features for the Bi-directional Long Short-Term Memory (Bi-LSTM) model. The Bi-LSTM model provides the time series prediction of air quality for four cities. The BSMO model obtained higher feature selection performance compared to existing techniques in air quality prediction. The BSMO-BILSTM model obtained 0.318 MSE, 0.564 RMSE, and 0.224 MAE, whereas Attention LSTM reached 0.699 MSE, 0.836 RMSE, and 0.892 MAE. Our solution may be of particular interest to various governmental and non-governmental institutions focused on maintaining high Quality of Life (QoL) on the local or state level.
Cytowania
-
1 4
CrossRef
-
0
Web of Science
-
1 8
Scopus
Autorzy (4)
-
Chelladurai Aarthi
-
Varatharaj Jeya Ramya
-
Parameshachari Bidare Divakarachari
Cytuj jako
Pełna treść
- Wersja publikacji
- Accepted albo Published Version
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/su15021637
- Licencja
-
otwiera się w nowej karcie
Słowa kluczowe
Informacje szczegółowe
- Kategoria:
- Publikacja w czasopiśmie
- Typ:
- artykuły w czasopismach
- Opublikowano w:
-
Sustainability
nr 15,
ISSN: - Język:
- angielski
- Rok wydania:
- 2023
- Opis bibliograficzny:
- Aarthi C., Jeya Ramya V., Falkowski-Gilski P., Bidare Divakarachari P.: Balanced Spider Monkey Optimization with Bi-LSTM for Sustainable Air Quality Prediction// Sustainability -Vol. 15,iss. 2 (2023), s.1637-
- DOI:
- Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/su15021637
- Źródła finansowania:
-
- Działalność statutowa/subwencja
- Weryfikacja:
- Politechnika Gdańska
wyświetlono 174 razy
Publikacje, które mogą cię zainteresować
Effective Air Quality Prediction Using Reinforced Swarm Optimization and Bi-Directional Gated Recurrent Unit
- S. Gurumoorthy,
- A. K. Kokku,
- P. Falkowski-Gilski
- + 1 autorów
An automated learning model for twitter sentiment analysis using Ranger AdaBelief optimizer based Bidirectional Long Short Term Memory
- S. Natarajan,
- S. Kurian,
- P. Bidare Divakarachari
- + 1 autorów
Feature Weighted Attention-Bidirectional Long Short Term Memory Model for Change Detection in Remote Sensing Images
- R. K. Patra,
- S. N. Patil,
- P. Falkowski-Gilski
- + 2 autorów
Intracranial hemorrhage detection in 3D computed tomography images using a bi-directional long short-term memory network-based modified genetic algorithm
- J. Sengupta,
- R. Alzbutas,
- P. Falkowski-Gilski
- + 1 autorów