Deep learning has emerged as a powerful tool for streamflow forecasting and its
applications have garnered significant interest in the hydrological community. Despite the …

River flow (Q flow) is a hydrological process that considerably impacts the management and
sustainability of water resources. The literature has shown great potential for nature-inspired …

Quickly and accurately gras** the water quality in the drainage network is essential for the
management and early warning of the urban water environment. Modeling-based detection …

PM 2.5 is a significant environmental pollutant that damages the environment and
endangers human health. Precise forecast of PM 2.5 concentrations is very important to …

Rapid and accurate detection of time-delayed water quality indicators (WQIs) is the key to
achieving fast feedback regulation of wastewater treatment plants (WWTPs) that enables its …

This study investigates the effectiveness of three deep learning methods, Long Short-Term
Memory (LSTM), Convolutional Neural Network (CNN), and Convolutional Long Short-Term …

Streamflow and flood forecasting remains one of the long-standing challenges in hydrology.
Traditional physically based models are hampered by sparse parameters and complex …

Global solar radiation (GSR) prediction plays an essential role in planning, controlling and
monitoring solar power systems. However, its stochastic behaviour is a significant challenge …

Prediction of electricity price is crucial for national electricity markets supporting sale prices,
bidding strategies, electricity dispatch, control and market volatility management. High …

Predicting electricity demand data is considered an essential task in decisions taking, and
establishing new infrastructure in the power generation network. To deliver a high-quality …