scholarly journals The effects of splitters on the downstream scour hole of overflow spillways: application of support vector regression

2021 ◽  
Author(s):  
Mohammad Ehsan Asadi ◽  
Seyed Taghi Omid Naeeni ◽  
Reza Kerachian
Keyword(s):  
Support Vector Regression ◽  
Experimental Models ◽  
Data Driven ◽  
Support Vector ◽  
Method Performance ◽  
Statistical Measures ◽  
Proposed Model ◽  
Scour Hole ◽  
Scour Hole Dimensions ◽  
Hole Dimensions

Abstract One of the most effective ways to reduce the water jet erosion power during dam overflow is to use splitters on the lower side of spillway. The dimensions of scouring holes and their location in the dam basin should be accurately determined. Experimental models and data driven techniques can be effectively used for estimating the dimensions of scouring holes. The focus of this paper is evaluating the effects of splitters on the downstream scour hole of overflow spillways and providing an optimized splitter configuration. The Support Vector Regression (SVR) method performance in predicting the scour hole dimensions and its location downstream of the dam has been examined using 116 experimental data. In order to evaluate the efficiency of the proposed model, we used different statistical measures. The results show that the presence of splitters decreases the slope of downstream scouring in all situations. It is also shown that the SVR method can accurately estimate the dimensions of the scour hole and its location.

scholarly journals Spatiotemporal Traffic Flow Prediction with KNN and LSTM

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Xianglong Luo ◽  
Danyang Li ◽  
Yu Yang ◽  
Shengrui Zhang
Keyword(s):  
Support Vector Regression ◽  
Traffic Flow ◽  
Prediction Models ◽  
Short Term Memory ◽  
Transportation Systems ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Traffic Flow Prediction ◽  
Flow Prediction ◽  
Proposed Model

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

scholarly journals Direction of Arrival Based on the Multioutput Least Squares Support Vector Regression Model

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Kai Huang ◽  
Ming-Yi You ◽  
Yun-Xia Ye ◽  
Bin Jiang ◽  
An-Nan Lu
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Direction Of Arrival ◽  
Direction Finding ◽  
Training Data ◽  
Data Driven ◽  
Support Vector ◽  
Support Vector Regression Model ◽  
Model Training ◽  
Finding Method

The interferometer is a widely used direction-finding system with high precision. When there are comprehensive disturbances in the direction-finding system, some scholars have proposed corresponding correction algorithms, but most of them require hypothesis based on the geometric position of the array. The method of using machine learning that has attracted much attention recently is data driven, which can be independent of these assumptions. We propose a direction-finding method for the interferometer by using multioutput least squares support vector regression (MLSSVR) model. The application of this method includes the following: the construction of MLSSVR model training data, training and construction of the MLSSVR model, and the estimation of direction of arrival. Finally, the method is verified through numerical simulation. When there are comprehensive deviations in the system, the direction-finding accuracy can be effectively improved.

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