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 …

Developing precise soft computing methods for groundwater management, which includes
quality and quantity, is crucial for improving water resources planning and management. In …

The management of water resources depends heavily on hydrological prediction, and
advances in machine learning (ML) present prospects for improving predictive modelling …

Precise estimation of water temperature plays a key role in environmental impact
assessment, aquatic ecosystems' management and water resources planning and …

Abstract Machine learning is one effective way of increasing the accuracy of sediment
transport models. Machine learning captures patterns in the sequence of both structured and …

Hybrid metaheuristic algorithm (MA), an advanced tool in the artificial intelligence field,
provides precise reference evapotranspiration (ETo) prediction that is highly important for …

Abstract The Water Framework Directive requires all water bodies in EU countries to ensure
the ecological integrity of freshwater ecosystems in all water bodies. A “good” status in water …

River streamflow is an essential hydrological parameters for optimal water resource
management. This study investigates models used to estimate monthly time-series river …

In multivariate time series forecasting tasks, expanding forecast length and improving
forecast efficiency is an urgent need for practical applications. Accurate long-term …

Forecasting daily runoff is of great importance to the allocation of water resources and flood
prevention. Many existing methods utilize identical networks to learn the long-term …