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Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research …
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 …
sustainability of water resources. The literature has shown great potential for nature-inspired …
A survey on river water quality modelling using artificial intelligence models: 2000–2020
There has been an unsettling rise in the river contamination due to the climate change and
anthropogenic activities. Last decades' research has immensely focussed on river basin …
anthropogenic activities. Last decades' research has immensely focussed on river basin …
Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks
Streamflow (Q flow) prediction is one of the essential steps for the reliable and robust water
resources planning and management. It is highly vital for hydropower operation, agricultural …
resources planning and management. It is highly vital for hydropower operation, agricultural …
The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction
Precise streamflow prediction is necessary for better planning and managing available
water and future water resources, especially for high altitude mountainous glacier melting …
water and future water resources, especially for high altitude mountainous glacier melting …
Performance comparison of an LSTM-based deep learning model versus conventional machine learning algorithms for streamflow forecasting
Streamflow forecasting plays a key role in improvement of water resource allocation,
management and planning, flood warning and forecasting, and mitigation of flood damages …
management and planning, flood warning and forecasting, and mitigation of flood damages …
Development of artificial intelligence for modeling wastewater heavy metal removal: State of the art, application assessment and possible future research
The presence of various forms of heavy metals (HMs)(eg, Cu, Cd, Pb, Zn, Cr, Ni, As, Co, Hg,
Fe, Mn, Sb, and Ce) in water bodies and sediment has been increasing due to industrial and …
Fe, Mn, Sb, and Ce) in water bodies and sediment has been increasing due to industrial and …
Prediction of risk delay in construction projects using a hybrid artificial intelligence model
Project delays are the major problems tackled by the construction sector owing to the
associated complexity and uncertainty in the construction activities. Artificial Intelligence (AI) …
associated complexity and uncertainty in the construction activities. Artificial Intelligence (AI) …
Support vector regression optimized by meta-heuristic algorithms for daily streamflow prediction
Accurate and reliable prediction of streamflow is vital to the optimization of water resources
management, reservoir flood operations, catchment, and urban water management. In this …
management, reservoir flood operations, catchment, and urban water management. In this …
Streamflow prediction using deep learning neural network: case study of Yangtze River
D Liu, W Jiang, L Mu, S Wang - IEEE access, 2020 - ieeexplore.ieee.org
The most important motivation for streamflow forecasts is flood prediction and longtime
continuous prediction in hydrological research. As for many traditional statistical models …
continuous prediction in hydrological research. As for many traditional statistical models …
A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer
Metaheuristic algorithms have received much attention recently for solving different
optimization and engineering problems. Most of these methods were inspired by nature or …
optimization and engineering problems. Most of these methods were inspired by nature or …