[PDF] Top 20 A Wavelet Support Vector Machine Combination Model for Daily Suspended Sediment Forecasting
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A Wavelet Support Vector Machine Combination Model for Daily Suspended Sediment Forecasting
... approaches, wavelet analysis and NF were applied to daily suspended sediment load prediction in a gauging station in the ...WNF model, selection of appropriate decomposed time series ... See full document
10
Application of Wavelet Denoising and Artificial Intelligence Models for Stream Flow Forecasting
... based wavelet denoising Least Square Support Vector Machine (LSSVM) and Artificial Neural Network (ANN) models were evaluated for forecasting daily Multi-Station (MS) streamflow ... See full document
8
Support Vector Machine and Least Square Support Vector Machine Stock Forecasting Models
... the wavelet-based forecasting models through which we would like to test the predictability of the models and compare those without the wavelet-based ...actual daily close value of Hong Kong ... See full document
10
Forecasting Crude Oil Prices using Discrete Wavelet Transform with Autoregressive Integrated Moving Average and Least Square Support Vector Machine Combination Approach
... series forecasting [14]. In this study, the researchers develop an ARIMA model for forecasting the monthly crude oil production in ...the model of (1,0,0) acquired during the empirical study ... See full document
9
Carbon Monoxide Prediction in the Atmosphere of Tehran Using Developed Support Vector Machine
... as support vector machine (SVM) has gained popularity in the machine learning community (Cristianini et ...fewer model parameters to be optimized in the SVM approach, reducing the ... See full document
15
Comparison and evaluation of intelligent models for river suspended sediment estimation (case study: Kakareza River, Iran)
... contrast, support vector machine (SVM) model is a nonlinear model and can be used to identify these ...to model a function with little information or incomplete ...and ... See full document
10
Ultra short term wind speed forecasting based on support vector machine with combined kernel function and similar data
... the wavelet kernel function are also deter- mined based on the error results of the cross-check of the training ...the wavelet kernel function to the optimal state and then set the combination ... See full document
7
Forecasting of River Sediment Amount using Machine Model
... using support vector ...of suspended sediment (SSL) per day in their ...used daily sediment and flow data from the Rantau Panjang station on the Johor ...of sediment by ... See full document
7
A Consistent Information Criterion for Support Vector Machines in Diverging Model Spaces
... true model is still largely ...this model selection consistency result relies heavily on the proper choice of the involved tuning parameter which is often selected by cross-validation in ... See full document
26
Application of LSSVM to logistics demand forecasting based on grey relational analysis and kernel principal component analysis
... for forecasting logistics demand. Some traditional forecasting methods, such as regression analysis and time series analysis establish forecasting model based on the mathematical theory and ... See full document
6
Analysis of Ammonia Nitrogen Content in Water Based on Weighted Least Squares Support Vector Machine (WLSSVM) Algorithm
... squares support vector machine (WLS-SVM) is applied to the analysis of ammonia nitrogen content, and the model is established to improve the accuracy of the ... See full document
7
Time Series Forecasting Using Wavelet Least Squares Support Vector Machines and Wavelet Regression Models for Monthly Stream Flow Data
... factory forecasting model such as LSSVM and LR mod- els is the selection of the input ...this model, even though a gen- eral framework can be followed based on previous suc- cessful application in ... See full document
12
An Support Vector Regression Based Nonlinear Modeling Method for Sic Mesfet
... LIBSVM-matlab code [14] is used as a basis to implement SVR model. v-SVR based on radial basis function (RBF) kernel function has been considered in our regression experiments. The parameters (ε, v, C and γ) are ... See full document
12
The Prediction Model of Financial Crisis Based on the Combination of Principle Component Analysis and Support Vector Machine
... prediction model with the method of Logistic Regression, and found that company size, capital structure, performance and current cash ability have remarkable prediction ...sion Model (LRM) is built on the ... See full document
9
Feature Space Selection and Combination for Native Language Identification
... Our results suggest that on the shared task, a combi- nation of features relying only on word and character ngrams provided a strong baseline. Our best system ended up being a combination of models trained on ... See full document
5
A hybrid model of self organizing maps and least square support vector machine for river flow forecasting
... flow forecasting is a major goal and an essential procedure that is necessary in water resource planning and ...many forecasting tech- niques used for river flow ...hybrid model based on a ... See full document
17
A Hybrid Method of Least Square Support Vector Machine and Bacterial Foraging Optimization Algorithm for Medium Term Electricity Price Forecasting
... price forecasting were designed by previous researchers such as regression models [7], generalized least squares model with auto-correlated residuals [8] and Autoregressive Moving Average Exogenous (ARMAX) ... See full document
8
Seasonal Based Electricity Demand Forecasting Using Time Series Analysis
... of forecasting as the initial process in power system engi- ...Demand forecasting plays a vital role in electricity ...through forecasting the power need and looking for the ways to acquire ...For ... See full document
10
A Two-Dimensional Mathematical Model of Suspended-Sediment Transport
... settling suspended sediments in Ptuj lake, based on the available meas- ured ...mathematical model and the measured values is relatively good (Figures 4 and 6); however, a calculation with a curvilinear ... See full document
12
Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model
... of model accuracy, among other predictors in order of their relative ...to model the SPI time-series, the greater importance of SST and the respective month in the model’s training period as a predictor was ... See full document
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