Daily water level forecasting is of significant importance for the comprehensive utilization of water resources. An improved least squares support vector machine (LSSVM) model was introduced by including an extra bias error control term in the objective function. The tuning parameters were determined by the cross-validation scheme. Both conventional and improved LSSVM models were applied in the short term forecasting of the water level in the middle reaches of the Yangtze River, China. Evaluations were made with both models through metrics such as RMSE (Root Mean Squared Error), MAPE (Mean Absolute Percent Error) and index of agreement (d). More accurate forecasts were obtained although the improvement is regarded as moderate. Results indicate the capability and flexibility of LSSVM-type models in resolving time sequence problems. The improved LSSVM model is expected to provide useful water level information for the managements of hydroelectric resources in Rivers. Energies 2019, 12, 112 2 of 11 artificial neural network (ANN) methods. Palani et al. [8] applied an ANN model for water quality estimation. Nourani et al. [9] established an ANN model for groundwater level prediction. Ivan and Gilja [10] showed good performance of ANNs for hydraulic parameter prediction. However, the model accuracy differs with neuron structures and parameter calibrations might be time-consuming.The support vector machine (SVM) has been used to address short-term forecasting problems since the 90s of the 20th century, on the basis of which LSSVM (least squares support vector machine) is put forward to overcome drawbacks (e.g., computation cost [11], uncertainties in structural parameter determination [12]) of SVM. LSSVM models solve a linear matrix equation with fewer constraint conditions and have been utilized in a variety of applications, e.g., forecasting of groundwater level fluctuations [13], river stage [14], and watershed runoff [15]. In the case of monthly flow forecasting, Noori et al. [16] discussed the influence of parameter selections on the model performance. Hybrid models have also been proved to be effective ways, such as SVM-Wavelet transform [17].Although the LSSVM models provide favorable solutions in hydrological forecasting problems, issues such as the kernel function and unbalanced features need to be carefully explored. Cheng et al. [18] improved LSSVM by integrating an adaptive time function. Thereby, the dynamic nature of the time series is considered by assigning an appropriate weight in the cash flow prediction for construction projects. To cope with low efficiency, Cong et al. [19] incorporated the fruit fly optimization algorithm (FOA) for appropriate parameter values of LSSVM. Comparison between LS-SVM-FOA and other models indicated the superiority of the improved model. Ghorbani et al.[20] modelled river discharge time series using SVM and ANN. The authors conclude that SVM and ANN have an edge over the results by the conventional RC (Rating Curve) and MLR (Multiple Linear Regression) models. This is mor...
Abstract-With the establishment of new curriculum standards, the study of English calls for "task based" learning. How to practically and effectively carry out reforms on English education and teaching in order to optimize the classroom teaching of this course, fully promote education for all-round development, cultivate the students for comprehensive language abilities and strengthen the guide on learning strategies as well as encourage them to develop their abilities for listening, speaking, reading and writing through experience, practice, discussions, cooperation, communication and exploration, has become key problems the teaching of English in high school that needs to be resolved urgently. Through the survey and research of the test materials, this paper offers some information about the outstanding advantages of multimedia computer-based classroom teaching by comparing with traditional teaching methods and provides empirical evidence for the effective application of modern teaching methods in classroom teaching.
The dynamic processes in the tidal reaches of the Yangtze River lead to the complexity of short-term water level forecasting. Historical data of daily water level are obtained for the lower reaches (Anqing-Wuhu-Nanjing) of the Yangtze River. Stationary time series of water level is derived by making the first-order difference with the raw datasets. An artificial neural network-Kalman hybrid model is proposed for water level forecasting, in which the Kalman filtering is introduced for partial data reconstruction. The model is calibrated with the hydrologic daily water level data of years 2014-2016 for MaAnshan station. Comparing with the traditional artificial neural network model, daily water level predictions are improved by the hybrid algorithm. Discrepancies appear under the circumstance of sharp variations of water level observations. Moreover, the implementation strategy of Kalman filtering algorithm is explored, which indicates the superiority of local Kalman filtering.
To select unlabeled example effectively and reduce classification error, confidence estimation for graph-based semi-supervised learning (CEGSL) is proposed. This algorithm combines graph-based semi-supervised learning with collaboration-training. It makes use of structure information of sample to calculate the classification probability of unlabeled example explicitly. With multi-classifiers, the algorithm computes the confidence of unlabeled example implicitly. With dual-confidence estimation, the unlabeled example is selected to update classifiers. The comparative experiments on UCI datasets indicate that CEGSL can effectively exploit unlabeled data to enhance the learning performance
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