Searching for Using Support Vector Machines For Long Term Discharge Prediction information? Find all needed info by using official links provided below.
https://www.tandfonline.com/doi/pdf/10.1623/hysj.51.4.599
Hydrological Sciences–Journal–des Sciences Hydrologiques, 51(4) August 2006 599 Using support vector machines for long-term discharge prediction JIAN-YI LIN1, CHUN-TIAN CHENG1 & KWOK-WING CHAU2 1 Institute of Hydroinformatics, Department of Civil Engineering, Dalian University of Technology, Dalian 116024, ChinaCited by: 435
https://www.academia.edu/239195/Using_support_vector_machines_for_long-term_discharge_prediction
"Accurate time- and site-specific forecasts of streamflow and reservoir inflow are important in effective hydropower reservoir management and scheduling. Traditionally, autoregressive moving-average (ARMA) models have been used in modelling
https://www.tandfonline.com/doi/abs/10.1623/hysj.51.4.599
Dec 25, 2009 · The SVM prediction model is tested using the long-term observations of discharges of monthly river flow discharges in the Manwan Hydropower Scheme. Through the comparison of its performance with those of the ARMA and ANN models, it is demonstrated that SVM is a very potential candidate for the prediction of long-term discharges.Cited by: 435
https://link.springer.com/article/10.1007/s00521-014-1626-9
May 29, 2014 · Relevance vector machines approach for long-term flow prediction. Authors; ... it is an alternative way to popular soft computing methods for long-term flow prediction providing at least comparable efficiency. ... Using support vector machines for long-term discharge prediction. Hydro Sci J 51(4):599–612 CrossRef Google Scholar. 3. Box GEP ...Author: Umut OkkanZafer
https://www.cs.princeton.edu/sites/default/files/uploads/saahil_madge.pdf
Predicting Stock Price Direction using Support Vector Machines Saahil Madge Advisor: Professor Swati Bhatt Abstract Support Vector Machine is a machine learning technique used in recent studies to forecast stock ... in the long-term we are able to reach prediction …Cited by: 8
https://www.sciencedirect.com/science/article/pii/S0022169414000845
Apr 16, 2014 · Monthly streamflow prediction using modified EMD-based support vector machine. ... especially in long term forecasting. ... H.S. Lü, X.L. Fu, L. Xiang, Y.H. ZhuA multi-layer soil moisture data assimilation using support vector machines and ensemble particle filter. J. …Cited by: 127
https://www.sciencedirect.com/science/article/pii/S002216941000819X
The purpose of this paper is to investigate the performance of wavelet-support vector machine conjunction model for monthly streamflow forecasting and to compare this with the performance of single support vector machine (SVM) models. 2. Support vector machines (SVMs)Cited by: 213
https://link.springer.com/article/10.1007%2Fs12665-016-5435-6
Apr 11, 2016 · Discharge time series were investigated using predictive models of support vector machine (SVM) and artificial neural network (ANN) and their performances were compared with two conventional models: rating curve (RC) and multiple linear regression (MLR) techniques. These models are evaluated using stage and discharge data from Big Cypress River, Texas, USA. Daily river stage–discharge …
https://link.springer.com/article/10.1007/s11431-014-5712-0
Nov 26, 2014 · Monthly discharge forecasting using wavelet neural networks with extreme learning machine ... Hence, how to obtain more appropriate parameters for feedforward neural networks with more precise prediction within shorter time has been a challenging task. ... Chau K W. Using support vector machines for long-term discharge prediction. Hydrolog Sci ...Cited by: 27
https://www.iit.demokritos.gr/sites/default/files/paper_6.pdf
A Comparison of Supervised Machine Learning Techniques for Predicting Short-Term In-Hospital Length of Stay Among Diabetic Patients April Mortona* ... support vector machines, multi-task learning, ... predict short-term vs. long-term LOS of each patient, where
How to find Using Support Vector Machines For Long Term Discharge Prediction information?
Follow the instuctions below:
- Choose an official link provided above.
- Click on it.
- Find company email address & contact them via email
- Find company phone & make a call.
- Find company address & visit their office.
