An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research …

ZM Yaseen, SO Sulaiman, RC Deo, KW Chau - Journal of Hydrology, 2019 - Elsevier
Despite the massive diversity in the modeling requirements for practical hydrological
applications, there remains a need to develop more reliable and intelligent expert systems …

Artificial intelligence based models for stream-flow forecasting: 2000–2015

ZM Yaseen, A El-Shafie, O Jaafar, HA Afan, KN Sayl - Journal of Hydrology, 2015 - Elsevier
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 …

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 …

Genetic programming in water resources engineering: A state-of-the-art review

AD Mehr, V Nourani, E Kahya, B Hrnjica, AMA Sattar… - Journal of …, 2018 - Elsevier
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 …

[HTML][HTML] Streamflow prediction with large climate indices using several hybrid multilayer perceptrons and copula Bayesian model averaging

F Panahi, M Ehteram, AN Ahmed, YF Huang… - Ecological …, 2021 - Elsevier
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 …

[HTML][HTML] Non-tuned machine learning approach for hydrological time series forecasting

ZM Yaseen, MF Allawi, AA Yousif, O Jaafar… - Neural Computing and …, 2018 - Springer
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 …

Univariate streamflow forecasting using commonly used data-driven models: literature review and case study

Z Zhang, Q Zhang, VP Singh - Hydrological Sciences Journal, 2018 - Taylor & Francis
Eight data-driven models and five data pre-processing methods were summarized; the
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

O Kisi, B Choubin, RC Deo… - Hydrological Sciences …, 2019 - Taylor & Francis
Understanding streamflow patterns by incorporating climate signal information can
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

M Ehteram, HA Afan, M Dianatikhah, AN Ahmed… - Water, 2019 - mdpi.com
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 …

[HTML][HTML] The influence of climatic inputs on stream-flow pattern forecasting: case study of Upper Senegal River

L Diop, A Bodian, K Djaman, ZM Yaseen… - Environmental earth …, 2018 - Springer
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 …