Enhancing PM2.5 Air Pollution Prediction Performance by Optimizing the Echo State Network (ESN) Deep Learning Model Using New Metaheuristic Algorithms DOI Creative Commons
Iman Zandi,

Ali Jafari,

Aynaz Lotfata

et al.

Urban Science, Journal Year: 2025, Volume and Issue: 9(5), P. 138 - 138

Published: April 23, 2025

Air pollution presents significant risks to both human health and the environment. This study uses air meteorological data develop an effective deep learning model for hourly PM2.5 concentration predictions in Tehran, Iran. evaluates efficient metaheuristic algorithms optimizing hyperparameters improve accuracy of predictions. The optimal feature set was selected using Variance Inflation Factor (VIF) Boruta-XGBoost methods, which indicated elimination NO, NO2, NOx. highlighted PM10 as most important feature. Wavelet transform then applied extract 40 features enhance prediction accuracy. Hyperparameters weights matrices Echo State Network (ESN) were determined algorithms, with Salp Swarm Algorithm (SSA) demonstrating superior performance. evaluation different criteria revealed that ESN-SSA outperformed other hybrids original ESN, LSTM, GRU models.

Language: Английский

Quadruple Strategy-Driven Hiking Optimization Algorithm for Low and High-Dimensional Feature Selection and Real-World Skin Cancer Classification DOI
Mahmoud Abdel-Salam, Saleh Ali Alomari,

Mohammad H. Almomani

et al.

Knowledge-Based Systems, Journal Year: 2025, Volume and Issue: unknown, P. 113286 - 113286

Published: March 1, 2025

Language: Английский

Citations

0
Enhancing PM2.5 Air Pollution Prediction Performance by Optimizing the Echo State Network (ESN) Deep Learning Model Using New Metaheuristic Algorithms DOI Creative Commons
Iman Zandi,

Ali Jafari,

Aynaz Lotfata

et al.

Urban Science, Journal Year: 2025, Volume and Issue: 9(5), P. 138 - 138

Published: April 23, 2025

Air pollution presents significant risks to both human health and the environment. This study uses air meteorological data develop an effective deep learning model for hourly PM2.5 concentration predictions in Tehran, Iran. evaluates efficient metaheuristic algorithms optimizing hyperparameters improve accuracy of predictions. The optimal feature set was selected using Variance Inflation Factor (VIF) Boruta-XGBoost methods, which indicated elimination NO, NO2, NOx. highlighted PM10 as most important feature. Wavelet transform then applied extract 40 features enhance prediction accuracy. Hyperparameters weights matrices Echo State Network (ESN) were determined algorithms, with Salp Swarm Algorithm (SSA) demonstrating superior performance. evaluation different criteria revealed that ESN-SSA outperformed other hybrids original ESN, LSTM, GRU models.

Language: Английский

Citations

0