Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research …
H Tao, SI Abba, AM Al-Areeq, F Tangang… - … Applications of Artificial …, 2024 - Elsevier
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 …
Artificial intelligence based models for stream-flow forecasting: 2000–2015
Summary The use of Artificial Intelligence (AI) has increased since the middle of the 20th
century as seen in its application in a wide range of engineering and science problems. The …
century as seen in its application in a wide range of engineering and science problems. The …
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 …
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 …
A novel attention-based LSTM cell post-processor coupled with bayesian optimization for streamflow prediction
Streamflow forecasting is critical for real-time water resources management and flood early
warning. In this study, we introduce a novel attention-based Long-Short Term Memory …
warning. In this study, we introduce a novel attention-based Long-Short Term Memory …
Streamflow and rainfall forecasting by two long short-term memory-based models
Prediction of streamflow and rainfall is important for water resources planning and
management. In this study, we developed two hybrid models, based on long short-term …
management. In this study, we developed two hybrid models, based on long short-term …
Applications of hybrid wavelet–artificial intelligence models in hydrology: a review
Accurate and reliable water resources planning and management to ensure sustainable use
of watershed resources cannot be achieved without precise and reliable models …
of watershed resources cannot be achieved without precise and reliable models …
Simulation and forecasting of streamflows using machine learning models coupled with base flow separation
Efficient simulation of rainfall-runoff relationships is one of the most complex problems owing
to the high number of interrelated hydrological processes. It is well-known that machine …
to the high number of interrelated hydrological processes. It is well-known that machine …
Generating ensemble streamflow forecasts: A review of methods and approaches over the past 40 years
Ensemble forecasting applied to the field of hydrology is currently an established area of
research embracing a broad spectrum of operational situations. This work catalogs the …
research embracing a broad spectrum of operational situations. This work catalogs the …
A robust method for non-stationary streamflow prediction based on improved EMD-SVM model
E Meng, S Huang, Q Huang, W Fang, L Wu, L Wang - Journal of hydrology, 2019 - Elsevier
Monthly streamflow prediction can offer important information for optimal management of
water resources, flood mitigation, and drought warning. The semi-humid and semi-arid Wei …
water resources, flood mitigation, and drought warning. The semi-humid and semi-arid Wei …