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This paper presents MORSE (MOdel for Rainfall Statistics Estimation), a unified model for the prediction of spatial and temporal high-resolution rainfall rate statistics. Inputs to MORSE are the convective and total rain amounts cumulated in different time intervals, ranging from a few hours for the prediction of to much longer intervals...
Time series data with abundant number of zeros are common in many applications, including climate and ecological modeling, disease monitoring, manufacturing defect detection, and traffic accident monitoring. Classical regression models are inappropriate to handle data with such skewed distribution because they tend to underestimate the frequency of zeros and the magnitude of non-zero values in the...
Rainfall prediction is a key question in the study field of hydrology and water resources. Point to non-linear, chaotic character and with the noise characteristics Run-off signals, we propose a new model based on empirical mode decomposition (EMD) and the RBF neural network (RBF). First, rainfall time series will be broken down into a series of different scales intrinsic mode function imf by EMD,...
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