A wavelet-support vector machine conjunction model for monthly streamflow forecasting

By Ozgur Kisi, Mesut Cimen. 2011

308 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Environmental Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/5a2ebcfa9294aaf33e9aa3a89da4a8e8bf109b68

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In Electrical and Electronic Engineering

In Environmental Engineering

In Management Science and Operations Research

Citations in the corpus

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, methodology): “To improve the modeling accuracy of the SVR model, Guo et al. (2011) and Kisi and Cimen (2011) both combined the wavelet and the SVR model in river flow forecasting problems where the two results both revealed that the improved SVR model combined with wavelet analysis can forecast hydrological data…”
A multivariate conditional model for streamflow prediction and spatial precipitation refinement (2015, methodology): “Kisi and Cimen [2011] developed a hybrid model that combines the wavelet transform with SVR for monthly streamflow forecasting, and Fallah-Mehdipour et al. [2012] examined the real-time operation of a reservoir system using GP.”
Lake Level Forecasting Using Wavelet-SVR, Wavelet-ANFIS and Wavelet-ARMA Conjunction Models (2015, background): “Empirical findings and theoretical suggestions showed that hybrid techniques could be effective in improving modelling (Kisi and Cimen 2011).”
Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada (2012, unknown): “And ﬁnally, the combination of wavelet transforms and SVMs have provided good forecasting results in hydrology [ Kisi and Cimen , 2011].”
Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review (2014, methodology): “In a similar study, Kisi and Cimen (2011) investigated the accuracy of a combined wavelet and SVM model in forecasting monthly stream-flow.”
Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model (2017, background): “2009; Wang and Ding 2003) whereas others have removed the db1 sub-series and added the remaining series, considering the former series as noise due to low correlation with their original data (Partal and Kişi 2007; Rajaee et al. 2010; Kisi and Cimen 2011).”
Modelling of impact of water quality on recharging rate of storm water filter system using various kernel function based regression (2018, methodology): “Within last few decades, soft computing approaches like neural network, Gaussian process, Support vector machines, ANFIS and M5 model tree have been used in environment and hydraulics applications (Kisi and Cimen 2011; Singh et al.”
Evaluating a coupled discrete wavelet transform and support vector regression for daily and monthly streamflow forecasting (2014, methodology): “,yn} (Kisi and Cimen, 2011) (here, the input and desired vectors refer to the daily or monthly streamflow records and n is the total number of data points).”
A hybrid support vector regression framework for streamflow forecast (2019, methodology): “Time series decomposition has the ability to analyze streamflow temporal and spatial variation and extracting useful information as much as possible (Kisi, 2011).”
Wavelet Genetic Algorithm-Support Vector Regression (Wavelet GA-SVR) for Monthly Flow Forecasting (2015, unknown): “Kisi and Cimen (2011) used discrete wavelet transform and support vector machine (SVM) conjunction models for monthly stream flow forecasting and found that the conjunction models could increase the forecast accuracy of the support vector machine.”
Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models (2014, background): “The c parameter is a constant that reduces the model space and controls the complexity of the solution, while C is a positive constant that is a capacity control parameter, and e is the loss function that describes the regression vector without all the input data (Kisi and Cimen, 2011).”