An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research …
Despite the massive diversity in the modeling requirements for practical hydrological
applications, there remains a need to develop more reliable and intelligent expert systems …
applications, there remains a need to develop more reliable and intelligent expert systems …
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 …
Prediction of Yangtze River streamflow based on deep learning neural network with El Niño–Southern Oscillation
S Ha, D Liu, L Mu - Scientific reports, 2021 - nature.com
Accurate long-term streamflow and flood forecasting have always been an important
research direction in hydrology research. Nowadays, climate change, floods, and other …
research direction in hydrology research. Nowadays, climate change, floods, and other …
Genetic programming in water resources engineering: A state-of-the-art review
The state-of-the-art genetic programming (GP) method is an evolutionary algorithm for
automatic generation of computer programs. In recent decades, GP has been frequently …
automatic generation of computer programs. In recent decades, GP has been frequently …
[HTML][HTML] Streamflow prediction with large climate indices using several hybrid multilayer perceptrons and copula Bayesian model averaging
Streamflow prediction help the modelers to manage water resources in watersheds. It gives
essential information for flood control and reservoir operation. This study uses the copula …
essential information for flood control and reservoir operation. This study uses the copula …
[HTML][HTML] Non-tuned machine learning approach for hydrological time series forecasting
Stream-flow forecasting is a crucial task for hydrological science. Throughout the literature,
traditional and artificial intelligence models have been applied to this task. An attempt to …
traditional and artificial intelligence models have been applied to this task. An attempt to …
Univariate streamflow forecasting using commonly used data-driven models: literature review and case study
Eight data-driven models and five data pre-processing methods were summarized; the
multiple linear regression (MLR), artificial neural network (ANN) and wavelet decomposition …
multiple linear regression (MLR), artificial neural network (ANN) and wavelet decomposition …
Incorporating synoptic-scale climate signals for streamflow modelling over the Mediterranean region using machine learning models
Understanding streamflow patterns by incorporating climate signal information can
contribute remarkably to the knowledge of future local environmental flows. Three machine …
contribute remarkably to the knowledge of future local environmental flows. Three machine …
Assessing the predictability of an improved ANFIS model for monthly streamflow using lagged climate indices as predictors
The current study investigates the effect of a large climate index, such as NINO3, NINO3. 4,
NINO4 and PDO, on the monthly stream flow in the Aydoughmoush basin (Iran) based on an …
NINO4 and PDO, on the monthly stream flow in the Aydoughmoush basin (Iran) based on an …
[HTML][HTML] The influence of climatic inputs on stream-flow pattern forecasting: case study of Upper Senegal River
Ideal prediction and modeling of stream-flow and its hydrological applications are extremely
significant for decision-making tasks and proper planning of water resource and hydraulic …
significant for decision-making tasks and proper planning of water resource and hydraulic …