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On the basis of our realization to the world, granulation is one of the basis concepts. It refers to the whole divides into the parts. Information granulation has a key role in many methods and technical domains. The theory of fuzzy information granulation (TFIG) is elicited by man's information granulation method and based this to infer. Support vector machine (SVM) is an effective method to predict...
Hydrology time series prediction is significant. It is not only helpful to set the planning in daily configuration works of water resources, but also provides guidance for leaders to make decision, especially in some special case such as flood and seriously lack water. In order to solve the imbalance complexity of prediction model and complexity of samples and raise forecasting accuracy, combined...
Air temperature is closely related to life and affects all aspects of life. Therefore, the forecast of the temperature is more far-reaching. In this paper, a new model based on EMD (Empirical Mode Decomposition) and LS-SVM (Least Squares Support Vector Machine) was proposed. At first, EMD was applied to adaptively decomposing the time series into a series of different scales of intrinsic mode function...
MODIS data play an important role in global and regional environment and resources researches. Remote image classification often fails to separate a large number of land cover classes that may present similar spectral reflectance. To improve the classification accuracy in such situations, multi-temporal data has been proved as valuable auxiliary information. In this paper, we used 250m MODIS/NDVI...
Through analyzing problem of Wavelet Transform, the annual precipitation series from 1956 to 2000 of the two sub-water resources regions of upper Lanzhou are decomposed into multiple time scale series with EMD method. The results show that the precipitation series have periods that about 3, 4~8, 11 and 22 years. A LS-SVM forecast model of precipitation and runoff based on EMD is established and then...
The paper presents the neural network approach to the accurate forecasting of the daily average concentration of PM10. Few neural predictors are applied: the multilayer perceptron, radial basis function, Elman network and support vector machine. They are used for prediction either in direct application or in combination with wavelet decomposition, forming many individual prediction results that will...
Based on the powerful nonlinear mapping ability of support vector machines, the predicting model of support vector machines in combination with takens' delay coordinate phase reconstruction of chaotic time series has been established. Yearly precipitation time series is of the chaotic characters, thus this model is used to try predicting the precipitation. Because of the peculiarity of precipitation...
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