In this study, potential of Least Square-Support Vector Regression (LS-SVR) approach is utilized to model the daily variation of river flow. Inherent complexity, unavailability of reasonably long data set and heteroge...In this study, potential of Least Square-Support Vector Regression (LS-SVR) approach is utilized to model the daily variation of river flow. Inherent complexity, unavailability of reasonably long data set and heterogeneous catchment response are the couple of issues that hinder the generalization of relationship between previous and forthcoming river flow magnitudes. The problem complexity may get enhanced with the influence of upstream dam releases. These issues are investigated by exploiting the capability of LS-SVR–an approach that considers Structural Risk Minimization (SRM) against the Empirical Risk Minimization (ERM)–used by other learning approaches, such as, Artificial Neural Network (ANN). This study is conducted in upper Narmada river basin in India having Bargi dam in its catchment, constructed in 1989. The river gauging station–Sandia is located few hundred kilometer downstream of Bargi dam. The model development is carried out with pre-construction flow regime and its performance is checked for both pre- and post-construction of the dam for any perceivable difference. It is found that the performances are similar for both the flow regimes, which indicates that the releases from the dam at daily scale for this gauging site may be ignored. In order to investigate the temporal horizon over which the prediction performance may be relied upon, a multistep-ahead prediction is carried out and the model performance is found to be reasonably good up to 5-day-ahead predictions though the performance is decreasing with the increase in lead-time. Skills of both LS-SVR and ANN are reported and it is found that the former performs better than the latter for all the lead-times in general, and shorter lead times in particular.展开更多
针对辅助驾驶汽车在行驶过程中对前方车辆识别的实时性差、效率低等问题,提出基于方向梯度直方图(histogram of oriented gradient,HOG)特征提取与模糊支持向量机结合的一种前车识别系统。对汽车行驶过程中的图像灰度化、二值化等预处理...针对辅助驾驶汽车在行驶过程中对前方车辆识别的实时性差、效率低等问题,提出基于方向梯度直方图(histogram of oriented gradient,HOG)特征提取与模糊支持向量机结合的一种前车识别系统。对汽车行驶过程中的图像灰度化、二值化等预处理后,进行HOG特征提取。对模糊支持向量机算法进行研究,通过增加模糊度变量的优化来选择最优分类决策面,使其对每个训练的正、负样本集的区域特征进行分类后识别。实验结果显示:模糊支持向量机算法不仅能够降低训练时的噪声,与支持向量机相比增强了支持向量,而且提高了训练时间与准确率;在能见度低的情况下识别效果较好,满足前车实时识别。展开更多
文摘In this study, potential of Least Square-Support Vector Regression (LS-SVR) approach is utilized to model the daily variation of river flow. Inherent complexity, unavailability of reasonably long data set and heterogeneous catchment response are the couple of issues that hinder the generalization of relationship between previous and forthcoming river flow magnitudes. The problem complexity may get enhanced with the influence of upstream dam releases. These issues are investigated by exploiting the capability of LS-SVR–an approach that considers Structural Risk Minimization (SRM) against the Empirical Risk Minimization (ERM)–used by other learning approaches, such as, Artificial Neural Network (ANN). This study is conducted in upper Narmada river basin in India having Bargi dam in its catchment, constructed in 1989. The river gauging station–Sandia is located few hundred kilometer downstream of Bargi dam. The model development is carried out with pre-construction flow regime and its performance is checked for both pre- and post-construction of the dam for any perceivable difference. It is found that the performances are similar for both the flow regimes, which indicates that the releases from the dam at daily scale for this gauging site may be ignored. In order to investigate the temporal horizon over which the prediction performance may be relied upon, a multistep-ahead prediction is carried out and the model performance is found to be reasonably good up to 5-day-ahead predictions though the performance is decreasing with the increase in lead-time. Skills of both LS-SVR and ANN are reported and it is found that the former performs better than the latter for all the lead-times in general, and shorter lead times in particular.
文摘针对辅助驾驶汽车在行驶过程中对前方车辆识别的实时性差、效率低等问题,提出基于方向梯度直方图(histogram of oriented gradient,HOG)特征提取与模糊支持向量机结合的一种前车识别系统。对汽车行驶过程中的图像灰度化、二值化等预处理后,进行HOG特征提取。对模糊支持向量机算法进行研究,通过增加模糊度变量的优化来选择最优分类决策面,使其对每个训练的正、负样本集的区域特征进行分类后识别。实验结果显示:模糊支持向量机算法不仅能够降低训练时的噪声,与支持向量机相比增强了支持向量,而且提高了训练时间与准确率;在能见度低的情况下识别效果较好,满足前车实时识别。
