scholarly journals Forecasting Daily River Flow Using an Artificial Flora–Support Vector Machine Hybrid Modeling Approach (Case Study: Karkheh Catchment, Iran)

2020 ◽  
Vol 13 ◽  
pp. 117862212096965
Author(s):  
Reza Dehghani ◽  
Hassan Torabi Poudeh ◽  
Hojatolah Younesi ◽  
Babak Shahinejad
Keyword(s):  
Support Vector Machine ◽  
River Flow ◽  
Evaluation Criteria ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Flow Modeling ◽  
Machine Model ◽  
Daily Discharge

In this study, the hybrid support vector machine–artificial flora algorithm method was developed and the obtained results were compared with those of the support vector–wave vector machine model. Karkheh catchment area was considered as a case study to estimate the flow rate of rivers using the daily discharge statistics taken from hydrometric stations located upstream of the dam in the statistical period of 2008 to 2018. Necessary criteria including coefficient of determination, root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe coefficient were used to evaluate and compare the models. The results illustrated that the combined structures provided acceptable results in terms of river flow modeling. Also, a comparison of the models based on the evaluation criteria and Taylor’s diagram demonstrated that the proposed hybrid method with the correlation coefficient of R2 = 0.924 to 0.974, RMSE = 0.022 to 0.066 m3/s, MAE = 0.011 to 0.034 m3/s, and Nash-Sutcliffe (NS) coefficient = 0.947 to 0.986 outperformed other methods in terms of estimating the daily flow rates of rivers.

scholarly journals Predicting Ink Transfer Rate of 3D Additive Printing Using EGBO Optimized Least Squares Support Vector Machine Model

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Shengpu Li ◽  
Yize Sun
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Least Squares ◽  
Transfer Rate ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Experimental Sample ◽  
Machine Model ◽  
Ink Transfer

Ink transfer rate (ITR) is a reference index to measure the quality of 3D additive printing. In this study, an ink transfer rate prediction model is proposed by applying the least squares support vector machine (LSSVM). In addition, enhanced garden balsam optimization (EGBO) is used for selection and optimization of hyperparameters that are embedded in the LSSVM model. 102 sets of experimental sample data have been collected from the production line to train and test the hybrid prediction model. Experimental results show that the coefficient of determination (R2) for the introduced model is equal to 0.8476, the root-mean-square error (RMSE) is 6.6 × 10 (−3), and the mean absolute percentage error (MAPE) is 1.6502 × 10 (−3) for the ink transfer rate of 3D additive printing.

Research on SVM Line Loss Rate Prediction Based on Heuristic Algorithm

2013 ◽  
Vol 291-294 ◽  
pp. 2164-2168 ◽  
Author(s):  
Li Tian ◽  
Qiang Qiang Wang ◽  
An Zhao Cao
Keyword(s):  
Support Vector Machine ◽  
Heuristic Algorithm ◽  
Support Vector Machine Model ◽  
Loss Rate ◽  
Heuristic Algorithms ◽  
Support Vector ◽  
Machine Model ◽  
Rate Prediction ◽  
Line Loss

With the characteristic of line loss volatility, a research of line loss rate prediction was imperatively carried out. Considering the optimization ability of heuristic algorithm and the regression ability of support vector machine, a heuristic algorithm-support vector machine model is constructed. Case study shows that, compared with other heuristic algorithms’, the search efficiency and speed of genetic algorithm are good, and the prediction model is with high accuracy.

scholarly journals Comparative Analysis of Artificial Intelligence Models for Accurate Estimation of Groundwater Nitrate Concentration

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5763 ◽  
Author(s):  
Shahab S. Band ◽  
Saeid Janizadeh ◽  
Subodh Chandra Pal ◽  
Indrajit Chowdhuri ◽  
Zhaleh Siabi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Water Resource Management ◽  
Nitrate Concentration ◽  
Evaluation Criteria ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Accurate Estimation ◽  
Support Vector ◽  
Topographic Wetness Index ◽  
Agricultural Areas

Prediction of the groundwater nitrate concentration is of utmost importance for pollution control and water resource management. This research aims to model the spatial groundwater nitrate concentration in the Marvdasht watershed, Iran, based on several artificial intelligence methods of support vector machine (SVM), Cubist, random forest (RF), and Bayesian artificial neural network (Baysia-ANN) machine learning models. For this purpose, 11 independent variables affecting groundwater nitrate changes include elevation, slope, plan curvature, profile curvature, rainfall, piezometric depth, distance from the river, distance from residential, Sodium (Na), Potassium (K), and topographic wetness index (TWI) in the study area were prepared. Nitrate levels were also measured in 67 wells and used as a dependent variable for modeling. Data were divided into two categories of training (70%) and testing (30%) for modeling. The evaluation criteria coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the performance of the models used. The results of modeling the susceptibility of groundwater nitrate concentration showed that the RF (R2 = 0.89, RMSE = 4.24, NSE = 0.87) model is better than the other Cubist (R2 = 0.87, RMSE = 5.18, NSE = 0.81), SVM (R2 = 0.74, RMSE = 6.07, NSE = 0.74), Bayesian-ANN (R2 = 0.79, RMSE = 5.91, NSE = 0.75) models. The results of groundwater nitrate concentration zoning in the study area showed that the northern parts of the case study have the highest amount of nitrate, which is higher in these agricultural areas than in other areas. The most important cause of nitrate pollution in these areas is agriculture activities and the use of groundwater to irrigate these crops and the wells close to agricultural areas, which has led to the indiscriminate use of chemical fertilizers by irrigation or rainwater of these fertilizers is washed and penetrates groundwater and pollutes the aquifer.

scholarly journals Effect of Dimensionality Reductions Technique in Modelling and Forecasting River Flow

2018 ◽  
Vol 7 (4.30) ◽  
pp. 573
Author(s):  
Shuhaida Ismail ◽  
Ani Shabri ◽  
Aida Mustapha ◽  
Siraj Mohammed Pandhiani
Keyword(s):  
Support Vector Machine ◽  
River Flow ◽  
Principal Component ◽  
Absolute Error ◽  
Least Square ◽  
Support Vector ◽  
Accurate Information ◽  
Statistical Measures ◽  
Future Value ◽  
Predicted Values

The ability of obtain accurate information on future river flow is a fundamental key for water resources planning, and management. Traditionally, single models have been introduced to predict the future value of river flow. This paper investigates the ability of Principal Component Analysis as dimensionality reduction technique and combined with single Support Vector Machine and Least Square Support Vector Machine, referred to as PCA-SVM and PCA-LSSVM. This study also presents comparison between the proposed models with single models of SVM and LSSVM. These models are ranked based on four statistical measures namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Correlation Coefficient ( ), and Correlation of Efficiency (CE). The results shows that PCA combined with LSSVM has better performance compared to other models. The best ranked models are then measured using Mean of Forecasting Error (MFE) to determine its forecast rate. PCA-LSSVM proven to be better model as it also indicates a small percentage of under-predicted values compared to the observed river flow values of 0.89% for Tualang river while over-predicted by 2. 08% for Bernam river. The study concludes by recommending the PCA as dimension reduction approach combined with LSSVM for river flow forecasting due to better prediction results and stability than those achieved from single models  

scholarly journals An Improved Whale Algorithm for Support Vector Machine Prediction of Photovoltaic Power Generation

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 212
Author(s):  
Yu-Wei Liu ◽  
Huan Feng ◽  
Heng-Yi Li ◽  
Ling-Ling Li
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Output Power ◽  
Input Data ◽  
Support Vector Machine Model ◽  
Weather Conditions ◽  
Clean Energy ◽  
Support Vector ◽  
Machine Model ◽  
Photovoltaic Power

Accurate prediction of photovoltaic power is conducive to the application of clean energy and sustainable development. An improved whale algorithm is proposed to optimize the Support Vector Machine model. The characteristic of the model is that it needs less training data to symmetrically adapt to the prediction conditions of different weather, and has high prediction accuracy in different weather conditions. This study aims to (1) select light intensity, ambient temperature and relative humidity, which are strictly related to photovoltaic output power as the input data; (2) apply wavelet soft threshold denoising to preprocess input data to reduce the noise contained in input data to symmetrically enhance the adaptability of the prediction model in different weather conditions; (3) improve the whale algorithm by using tent chaotic mapping, nonlinear disturbance and differential evolution algorithm; (4) apply the improved whale algorithm to optimize the Support Vector Machine model in order to improve the prediction accuracy of the prediction model. The experiment proves that the short-term prediction model of photovoltaic power based on symmetry concept achieves ideal accuracy in different weather. The systematic method for output power prediction of renewable energy is conductive to reducing the workload of predicting the output power and to promoting the application of clean energy and sustainable development.

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