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In order to predict the changes of water level of natural river, this paper applied the method of LS-SVM, combining with sichuan zigong hydrologic measured water level data and flow data builded a double input single output (water level and flow-water level) river water level prediction model. Through the analysis of the prediction results, can draw the conclusion: The accuracy of the prediction model, which was established by LS-SVM is very high, can meet the needs of the river water level prediction.

Fuzzy time series (FTS) is an effective method in forecasting problems due to its salient capabilities of tracking uncertainty and vagueness in observation data. However, in FTS forecasting, it is required about 5-7 intervals in the universe of discourse, as a result, the method of partition intervals become a major consideration. Recently, some studies have demonstrated that the method using length-variant intervals is able to improve the forecasting accuracy at a large scale. In this paper, a K-means clustering technique is used while selecting the length of each interval, and then a novel model of FTS is introduced accordingly. The empirical results show that this model provides a more general platform for partitioning universe of discourse, and gets higher forecasting accuracy rates than those of the existing methods.

How to use the incremental training corpus to improve the question classification accuracy rate in the process of question classification based on statistic learning. A question classification method based on the incremental modified Bayes was presented in this paper. The method used the modified Bayes and combined the incremental learning to correct the parameter by the incremental training set stage by stage, and established the question classification model based on the incremental modified Bayes. A question classification experiment was done in the domain of Yunnan tourism, the experimental results showed that the presented method evidently excelled than the modified Bayes method in the accuracy rate and the training time, the average accuracy rate was improved 3.3 percentage points than the accuracy rate of the modified Bayes method; the average training time was improved 39.1 percentage points than the training time efficiency of the modified Bayes method.