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A novel extreme rainfall prediction model combined with data mining is proposed in this paper. Because of the special nature of hydrological data, our model uses the clustering method to group the next year's average extreme rainfall and then establish a hybrid extreme rainfall prediction model based on building neural networks for each group. Furthermore(More)
Traditional data mining algorithm had limited capacity at short-term hydrological forecasting with low accuracy, and made little use of the error between the data set and the results to correct the results. Considering of the traditional hydrology predictive algorithm combined only with the external associated factors, but had not fully excavated the(More)
Multivariate time series (MTS) datasets are very common in various financial, multimedia, and hydrological fields. In this paper, a dimension-combination method is proposed to search similar sequences for MTS. Firstly, the similarity of single-dimension series is calculated; then the overall similarity of the MTS is obtained by synthesizing each of the(More)
Traditional data mining predictive algorithm dealt little with original data set and did not make full use of the relationship of the data, as a result, numerous mathematical operation resources was wasted and also the accuracy of the predicted result was not very high. Against with this problem, correlation coefficient and information entropy were(More)
Large amount of hydrological data set is a kind of big data, which has much hidden and potentially useful knowledge. It is necessary to extract these knowledge from hydrological data set, which can provide more valuable hydrological information and be useful for future hydrological forecasting. Data mining based on time series is widely used currently.(More)
Large amount of hydrological data set is a kind of big data, which has much hidden and potentially useful knowledge. Hydrological prediction is important for the state flood control and drought relief. How to forecast accurately and timely with hydrological big data becomes a big challenge. There are some forecasting techniques used widely. However, they(More)