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Abstract Carbon markets play a critical role in emission reduction and sustainable economic transition; however, carbon price series exhibit high volatility, nonlinear dynamics, and complex cross-feature interactions, ma...

Abstract Accurate prediction of terrestrial water cycle variables like soil moisture (SM) and evapotranspiration (ET) is crucial, yet existing Deep Learning (DL) models face limitations. Many adopt Single-Task Learning (...

Abstract Air pollution, particularly Nitrogen Dioxide ( $$\text {NO}_2$$ ) and ground-level ozone ( $$\text {O}_3$$ ), poses major risks to human health and the environment, making accurate forecasting essential for effe...

Abstract To address the limitations of existing oil-well production forecasting models, particularly suboptimal hyperparameter selection, difficulties in fine-tuning, insufficient global search capability, and slow conve...

Abstract In response to the problems of large fluctuations in electricity prices and difficulty in long-term forecasting caused by the large-scale integration of new energy in the electricity market, this paper construct...

Abstract Proposed research presents a data-driven framework for forecasting municipal solid waste (MSW) generation and emission dynamics in Erode City, India, by employing supervised machine learning algorithms. Leveragi...

Abstract The storage and retrieval of wave spectra for boundary conditions in nested wave modeling are computationally intensive due to the substantial storage requirements of each spectrum. A recently developed method (...

Abstract Interannual variability in the runoff ratio—how much annual precipitation becomes streamflow—underpins water management, flood forecasting, and biogeochemical fluxes. In most hydrologic frameworks, this vari...