Performance evaluation of convolutional neural network and vision transformer models for groundwater potential mapping

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 132840 - 132840

Опубликована: Фев. 1, 2025

Язык: Английский

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

Urban Science, Год журнала: 2025, Номер 9(5), С. 138 - 138

Опубликована: Апрель 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.

Язык: Английский

Improving Human Brucellosis Susceptibility Mapping Using Effective and Simultaneously Metaheuristic-based Feature Selection and Hyperparameter Tuning

Acta Tropica, Год журнала: 2025, Номер unknown, С. 107657 - 107657

Опубликована: Май 1, 2025

Язык: Английский