scholarly journals A Forecasting Approach Combining Self-Organizing Map with Support Vector Regression for Reservoir Inflow during Typhoon Periods

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Gwo-Fong Lin ◽  
Tsung-Chun Wang ◽  
Lu-Hsien Chen
Keyword(s):  
Support Vector Regression ◽  
Support Vector ◽  
Self Organizing Map ◽  
Support Vector Regression Model ◽  
Proposed Model ◽  
Forecasting Performance ◽  
Short Lead Time ◽  
Reservoir Inflow ◽  
Reservoir Watershed ◽  
Self Organizing

This study describes the development of a reservoir inflow forecasting model for typhoon events to improve short lead-time flood forecasting performance. To strengthen the forecasting ability of the original support vector machines (SVMs) model, the self-organizing map (SOM) is adopted to group inputs into different clusters in advance of the proposed SOM-SVM model. Two different input methods are proposed for the SVM-based forecasting method, namely, SOM-SVM1 and SOM-SVM2. The methods are applied to an actual reservoir watershed to determine the 1 to 3 h ahead inflow forecasts. For 1, 2, and 3 h ahead forecasts, improvements in mean coefficient of efficiency (MCE) due to the clusters obtained from SOM-SVM1 are 21.5%, 18.5%, and 23.0%, respectively. Furthermore, improvement in MCE for SOM-SVM2 is 20.9%, 21.2%, and 35.4%, respectively. Another SOM-SVM2 model increases the SOM-SVM1 model for 1, 2, and 3 h ahead forecasts obtained improvement increases of 0.33%, 2.25%, and 10.08%, respectively. These results show that the performance of the proposed model can provide improved forecasts of hourly inflow, especially in the proposed SOM-SVM2 model. In conclusion, the proposed model, which considers limit and higher related inputs instead of all inputs, can generate better forecasts in different clusters than are generated from the SOM process. The SOM-SVM2 model is recommended as an alternative to the original SVR (Support Vector Regression) model because of its accuracy and robustness.

scholarly journals A Clustering-Based Hybrid Support Vector Regression Model to Predict Container Volume at Seaport Sanitary Facilities

2020 ◽  
Vol 10 (23) ◽  
pp. 8326
Author(s):  
Juan Jesús Ruiz-Aguilar ◽  
José Antonio Moscoso-López ◽  
Daniel Urda ◽  
Javier González-Enrique ◽  
Ignacio Turias
Keyword(s):  
Support Vector Regression ◽  
Linear Models ◽  
Hybrid Approach ◽  
Accurate Prediction ◽  
Support Vector ◽  
Self Organizing Map ◽  
Support Vector Regression Model ◽  
Proposed Model ◽  
Key Factor ◽  
Predicted Values

An accurate prediction of freight volume at the sanitary facilities of seaports is a key factor to improve planning operations and resource allocation. This study proposes a hybrid approach to forecast container volume at the sanitary facilities of a seaport. The methodology consists of a three-step procedure, combining the strengths of linear and non-linear models and the capability of a clustering technique. First, a self-organizing map (SOM) is used to decompose the time series into smaller clusters easier to predict. Second, a seasonal autoregressive integrated moving averages (SARIMA) model is applied in each cluster in order to obtain predicted values and residuals of each cluster. These values are finally used as inputs of a support vector regression (SVR) model together with the historical data of the cluster. The final prediction result integrates the prediction results of each cluster. The experimental results showed that the proposed model provided accurate prediction results and outperforms the rest of the models tested. The proposed model can be used as an automatic decision-making tool by seaport management due to its capacity to plan resources in advance, avoiding congestion and time delays.

scholarly journals Vehicular CO Emission Prediction Using Support Vector Regression Model and GIS

2018 ◽  
Vol 10 (10) ◽  
pp. 3434 ◽  
Author(s):  
Omer Azeez ◽  
Biswajeet Pradhan ◽  
Helmi Shafri
Keyword(s):  
Support Vector Regression ◽  
Urban Areas ◽  
Road Traffic ◽  
Spatial Prediction ◽  
Absolute Error ◽  
Support Vector ◽  
Vehicular Emissions ◽  
Support Vector Regression Model ◽  
Promising Tool ◽  
Proposed Model

Transportation infrastructures play a significant role in the economy as they provide accessibility services to people. Infrastructures such as highways, road networks, and toll plazas are rapidly growing based on changes in transportation modes, which consequently create congestions near toll plaza areas and intersections. These congestions exert negative impacts on human health and the environment because vehicular emissions are considered as the main source of air pollution in urban areas and can cause respiratory and cardiovascular diseases and cancer. In this study, we developed a hybrid model based on the integration of three models, correlation-based feature selection (CFS), support vector regression (SVR), and GIS, to predict vehicular emissions at specific times and locations on roads at microscale levels in an urban areas of Kuala Lumpur, Malaysia. The proposed model comprises three simulation steps: first, the selection of the best predictors based on CFS; second, the prediction of vehicular carbon monoxide (CO) emissions using SVR; and third, the spatial simulation based on maps by using GIS. The proposed model was developed with seven road traffic CO predictors selected via CFS (sum of vehicles, sum of heavy vehicles, heavy vehicle ratio, sum of motorbikes, temperature, wind speed, and elevation). Spatial prediction was conducted based on GIS modelling. The vehicular CO emissions were measured continuously at 15 min intervals (recording 15 min averages) during weekends and weekdays twice per day (daytime, evening-time). The model’s results achieved a validation accuracy of 80.6%, correlation coefficient of 0.9734, mean absolute error of 1.3172 ppm and root mean square error of 2.156 ppm. In addition, the most appropriate parameters of the prediction model were selected based on the CFS model. Overall, the proposed model is a promising tool for traffic CO assessment on roads.

scholarly journals Continuous Monitoring of the Ambient Factors via ε-Smooth Support Vector Regression

Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 63
Author(s):  
Yan-Ru Jhuo ◽  
Chi-Yu Chen ◽  
Yu-Hsuan Yang ◽  
Hsing-Chuan Hsieh ◽  
Yuh-Jye Lee
Keyword(s):  
Regression Model ◽  
Support Vector Regression ◽  
Continuous Monitoring ◽  
Three Dimensional ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Heat Map ◽  
Proposed Model ◽  
Whole Space ◽  
The Internet Of Things

Thanks to the advances of the Internet of Things (IoTs), more and more wireless sensor networks applications have been realized. One of the fundamental but crucial applications is the continuous monitoring of environmental factors including temperature, humidity, illumination, etc. We develop a nonlinear regression model which takes spatial and temporal information into account to construct a globally three-dimensional heat map for a closed space based on very sparse sensor deployment. However, fitting the whole-space heat map with a very limited number of sensor observations gives a very poor estimation when we use a nonlinear model. We call it the coverage hole problem. We utilize the uniform experimental design which is well known in industrial statistics to allocate the synthetic sensors. We estimate those synthetic sensor readings on the basis of linear model locally. We then apply ε -SSVR, a nonlinear support vector regression model to fit the globally three-dimensional heat map by combining real sensor and synthetic sensor readings. The numerical results demonstrate our proposed model can enhance the accuracy significantly.

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