scholarly journals Support Vector Machine and Neural Network based Model for Monthly Stream Flow Forecasting

2018 ◽  
Vol 7 (4.35) ◽  
pp. 683
Author(s):  
Nuratiah Zaini ◽  
Marlinda Abdul Malek ◽  
Marina Yusoff ◽  
Siti Fatimah Che Osmi ◽  
Nurul Hani Mardi ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Hybrid Model ◽  
Peninsular Malaysia ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Design Development ◽  
Streamflow Forecasting ◽  
Monthly Streamflow ◽  
Bpnn Model

Accurate forecasting of streamflow is desired in many water resources planning and management, flood prevention and design development. In this study, the accuracy of two hybrid model, support vector machine - particle swarm optimization (SVM-PSO) and bat algorithm – backpropagation neural network (BA-BPNN) for monthly streamflow forecasting at Kuantan River located in Peninsular Malaysia are investigated and compared to regular SVM and BPNN model. Heuristic optimization namely PSO and BA are introduced to find the optimum SVM and BPNN parameters. The input parameters to the forecasting models are antecedent streamflow, historical rainfall and meteorological parameters namely evaporation, temperature, relative humidity and mean wind speed. Two performance evaluation measure, root mean square error (RMSE) and coefficient of determination (R2) were employed to evaluate the performance of developed forecasting model. It is found that, RMSE and R2 for hybrid SVM-PSO are 24.8267 m3/s and 0.9651 respectively while general SVM model yields RMSE of 27.5086 m3/s and 0.9305 of R2 for testing phase. Besides that, hybrid BA-BPNN produces RMSE, 17.7579 m3/s and R2, 0.7740 while BPNN model produces lower RMSE and R2 of 28.1396 m3/s and 0.5015 respectively. Therefore, the results indicate that hybrid model, SVM-PSO and Bat-BPNN yield better performance as compared to general SVM and BPNN, respectively in streamflow forecasting.

scholarly journals Remote-Sensing-Based Streamflow Forecasting Using Artificial Neural Network and Support Vector Machine Models

2021 ◽  
Vol 13 (20) ◽  
pp. 4147
Author(s):  
Mohammed M. Alquraish ◽  
Mosaad Khadr
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Superior Performance ◽  
Support Vector ◽  
Streamflow Forecasting ◽  
Forecasting Models ◽  
Monthly Streamflow ◽  
Artificial Neural ◽  
Monthly Streamflow Forecasting

In this study, we aimed to investigate the hydrological performance of three gridded precipitation products—CHIRPS, RFE, and TRMM3B42V7—in monthly streamflow forecasting. After statistical evaluation, two monthly streamflow forecasting models—support vector machine (SVM) and artificial neural network (ANN)—were developed using the monthly temporal resolution data derived from these products. The hydrological performance of the developed forecasting models was then evaluated using several statistical indices, including NSE, MAE, RMSE, and R2. The performance measures confirmed that the CHIRPS product has superior performance compared to RFE 2.0 and TRMM data, and it could provide reliable rainfall estimates for use as input in forecasting models. Likewise, the results of the forecasting models confirmed that the ANN and SVM both achieved acceptable levels of accuracy for forecasting streamflow; however, the ANN model was superior (R2 = 0.898–0.735) to the SVM (R2 = 0.742–0.635) in both the training and testing periods.

Hybrid Model of Convolutional Neural Network and Support Vector Machine to Classify Basal Cell Carcinoma

2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Jorge Alexander Ángeles Rojas ◽  
Hugo D. Calderon Vilca ◽  
Ernesto N. Tumi Figueroa ◽  
Kent Jhunior Cuadros Ramos ◽  
Steve S. Matos Manguinuri ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Basal Cell Carcinoma ◽  
Convolutional Neural Network ◽  
Cell Carcinoma ◽  
Hybrid Model ◽  
Basal Cell ◽  
Support Vector

scholarly journals A comparative study of multiple linear regression, artificial neural network and support vector machine for the prediction of dissolved oxygen

2016 ◽  
Vol 48 (5) ◽  
pp. 1214-1225 ◽  
Author(s):  
Xue Li ◽  
Jian Sha ◽  
Zhong-liang Wang
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Linear Regression ◽  
Dissolved Oxygen ◽  
Multiple Linear Regression ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Physical Factors ◽  
Data Set ◽  
Important Indicator

Dissolved oxygen (DO) is an important indicator reflecting the healthy state of aquatic ecosystems. The balance between oxygen supply and consuming in the water body is significantly influenced by physical and chemical parameters. This study aimed to evaluate and compare the performance of multiple linear regression (MLR), back propagation neural network (BPNN), and support vector machine (SVM) for the prediction of DO concentration based on multiple water quality parameters. The data set included 969 samples collected from rivers in China and the 16 predicted variables involved physical factors, nutrients, organic substances, and metal ions, which would affect the DO concentrations directly or indirectly by influencing the water–air exchange, the growth of water plants, and the lives of aquatic animals. The models optimized by particle swarm optimization (PSO) algorithm were calibrated and tested, with nearly 80% and 20% data, respectively. The results showed that the PSO-BPNN and PSO-SVM had better predicted performances than linear regression methods. All of the evaluated criteria, including coefficient of determination, mean squared error, and absolute relative errors suggested that the PSO-SVM model was superior to the MLR and PSO-BPNN for DO prediction in the rivers of China with limited knowledge of other information.

scholarly journals A Conjunction Method of Wavelet Transform-Particle Swarm Optimization-Support Vector Machine for Streamflow Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Fanping Zhang ◽  
Huichao Dai ◽  
Deshan Tang
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Wavelet Transform ◽  
Correlation Coefficients ◽  
Support Vector ◽  
Streamflow Forecasting ◽  
Swarm Optimization ◽  
Svm Model ◽  
Monthly Streamflow

Streamflow forecasting has an important role in water resource management and reservoir operation. Support vector machine (SVM) is an appropriate and suitable method for streamflow prediction due to its best versatility, robustness, and effectiveness. In this study, a wavelet transform particle swarm optimization support vector machine (WT-PSO-SVM) model is proposed and applied for streamflow time series prediction. Firstly, the streamflow time series were decomposed into various details (Ds) and an approximation (A3) at three resolution levels (21-22-23) using Daubechies (db3) discrete wavelet. Correlation coefficients between eachDsubtime series and original monthly streamflow time series are calculated.Dscomponents with high correlation coefficients (D3) are added to the approximation (A3) as the input values of the SVM model. Secondly, the PSO is employed to select the optimal parameters,C,ε, andσ, of the SVM model. Finally, the WT-PSO-SVM models are trained and tested by the monthly streamflow time series of Tangnaihai Station located in Yellow River upper stream from January 1956 to December 2008. The test results indicate that the WT-PSO-SVM approach provide a superior alternative to the single SVM model for forecasting monthly streamflow in situations without formulating models for internal structure of the watershed.

scholarly journals Solving the pan evaporation process complexity using the development of multiple mode of neurocomputing models

2021 ◽  
Author(s):  
Mohammad Ali Ghorbani ◽  
Milad Alizadeh Jabehdar ◽  
Zaher Mundher Yaseen ◽  
Samed Inyurt
Keyword(s):  
Support Vector Machine ◽  
Water Resources ◽  
Water Resources Management ◽  
Hybrid Model ◽  
Computational Method ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Multiple Model ◽  
Pan Evaporation ◽  
Resources Management

Abstract Finding an accurate computational method for predicting pan evaporation (EP), can be useful in the application of these methods for the development of sustainable agricultural systems and water resources management. In the present study, the proposed hybrid method called Multiple Model-Support Vector Machine (MM-SVM) with the aim of increasing the accuracy of EP prediction on a monthly scale (EPm) in two meteorological stations (Ardabil and Khalkhal) using the output of artificial intelligence (AI) models (i.e., artificial neural network (ANN) and support vector machine (SVM)) were evaluated. The results of intelligent models using several statistical indices (i.e., root mean square error (RMSE), mean absolute error value (MAE), Kling-Gupta (KGE) and coefficient of determination (R2)) and with the help of case visual indicators Were compared. According to the results of evaluation indicators in the test phase, two models MM-SVM-6 and ANN-5 with (RMSE, MAE, KGE and R2 equal to 1.088, 0.761, 0.79, 0.54 mm. month− 1, 0.819, 0.903 and 0.939, 0.962) and with three input variables, were introduced as the top models in Ardabil and Khalkhal stations, respectively. The proposed hybrid model (MM-SVM) was able to use its multi-model strategy with inputs predicted by independent models, its power to predict EPm in scenarios where there is a high correlation between its components with EPm, in a feasible state Accept to show. So that the incremental, constant and decreasing modes in EPm prediction accuracy by this hybrid model under the above conditions (especially in Ardabil station) were quite clear. Therefore, the results of the proposed and superior models in the present study can help local stakeholders in discussing water resources management.

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