Streamflow Forecasting Using Different Artificial Neural Network Algorithms

By Ozgur Kisi. 2007

380 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/3f47ed0e11308f9c9bbb2fd070b24025430fb606

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In Electrical and Electronic Engineering

In Environmental Engineering

In Water Science and Technology

Citations in the corpus

Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data (2017, methodology): “There are many different learning rules for training the feed-forward neural networks, but the most often used is the Deltarule or backpropagation (BP) rule (Kisi 2007).”
Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment (2018, background): “For the hydrological modeling, both short term and long term forecasts of river flow events are considered important for the problem, Kişi (2007) presented a comparison of different ANNs algorithms (namely, back-propagation, conjugate gradient, cascade correlation, and Levenberg– Marquardt) for…”
Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes (2013, background): “…De Vos and Rientjes, 2005; Sudheer et al., 2002; Tokar and Johnson, 1999) for streamflow forecasting (Campolo et al., 1999; Firat and Gungor, 2007; Kisi, 2007; Riad et al., 2004; Turan and Yurdusev, 2009) and for ground water modelling (Coulibaly et al., 2001; Daliakopoulos et al., 2005; Rogers…”
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, methodology): “Hydrological forecasting studies have shown that this algorithm is generally the best performing training method for ANN (Kişi 2007; Adamowski and Karapataki 2010) as it combines the speed of the Newton algorithm with the stability of the steepest descent method.”
Estimation of Soil Cation Exchange Capacity using Multiple Regression, Artificial Neural Networks, and Adaptive Neuro-fuzzy Inference System Models in Golestan Province, Iran (2015, methodology): “Depending on the types of inference operations upon if-then rules, most FISs can be classified into three types: Tsukamoto’s system, Mamdani’s system, and Sugeno’s system (Kişi 2007).”
Flood Prediction Using Machine Learning Models: Literature Review (2018, methodology): “ost conventional models. References [77,78] provided reviews on ANN applications in flood. ANNs were already successfully used for numerous flood prediction applications, e.g., streamflow forecasting [79], river flow [80,81], rainfall–runoff [82], precipitation–runoff modeling [83], water quality [5”
Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm (2021, background): “In addition, both short-term and long-term streamflow forecasting is necessary pertaining to the optimization of the hydrological components of water resource systems mainly during flood or drought periods [9].”