A nature-inspired and noise-assisted feature extraction integrating spatiotemporal attention-based sequence2sequence for multivariate wind speed prediction - Sibtain, Muhammad; Li, Xianshan; Saleem, Snoober; Qurat-ul-Ain; Shi, Qiang; Li, Fei; Apaydin, Halit (Stochastic environmental research and risk assessment, 2025)
Keywords: Alternative energy sources; Aquatic Pollution; Artificial intelligence; Attention; Chemistry and Earth Sciences; Computational Intelligence; Computer Science; Correlation analysis; Deep learning; Earth and Environmental Science; Earth Sciences; Efficiency; Emissions; Energy industry; Environment; Environmental research; Feature extraction; Greenhouse gases; Irradiance; Machine learning; Math. Appl. in Environmental Science; Multivariate analysis; Neural networks; Noise prediction; Optimization algorithms; Original Paper; Performance measurement; Physics; Prediction models; Probability Theory and Stochastic Processes; Renewable resources; Risk assessment; Solar radiation; Spatiotemporal data; Statistics for Engineering; Support vector machines; Temporal variations; Time series; Waste Water Technology; Water Management; Water Pollution Control; Wavelet transforms; Wind power; Wind speed; Multivariate wind speed prediction; ICEEMDAN; Harris Hawks optimizer; Spatiotemporal attention; Sequence2sequence