Meteorological drought prediction method and device based on VMD-CNN-BiLSTM-ATT hybrid model

Abstract

The invention belongs to the technical field of meteorological drought prediction, and particularly relates to a meteorological drought prediction method and device based on a VMD-CNN-BiLSTM-ATT hybrid model, and the method comprises the steps: obtaining historical meteorological data, taking the historical meteorological data as input data, carrying out the variational mode decomposition of the input data, obtaining a plurality of intrinsic mode components IMF, respectively splitting each IMF component into a training set and a test set; inputting data of the training set into an input layer of the convolutional neural network, and calculating to obtain an output matrix; taking a matrix obtained by pooling as the input of a bidirectional long-short-term memory network, processing data from the forward direction and the reverse direction at the same time, and paying attention to the correlation between the future moment and the current moment; adding an attention mechanism layer to the output side of the bidirectional long-short-term memory network, adding weights to the hidden layer feature vectors, and calculating output data, namely predicted values, again; subjecting all CNN-BiLSTM-ATT predicted values to recombination and superposition, and obtaining an output sequence. Compared with a traditional drought prediction method, the method is smaller in prediction error and higher in prediction precision and credibility.

Description

technical field [0001] The invention belongs to the technical field of meteorological drought forecasting, and in particular relates to a meteorological drought forecasting method and device based on a VMD-CNN-BiLSTM-ATT hybrid model. Background technique [0002] Drought is one of the most common and complex natural disasters, and it is also one of the most serious meteorological disasters affecting human society. Compared with other natural disasters, drought develops slowly, its characteristics are not easy to quantify, its impact mode is direct, and its damage area is large. Accurate and reliable meteorological drought prediction can bring great benefits to water resources management and modern smart water conservancy. However, the three characteristics of instability, jumping and randomness make it very difficult to predict drought accurately. [0003] Under the influence of climate change and human activities, the rainfall process has high variability, which poses gr...

AI Technical Summary

Problems solved by technology

However, the three characteristics of instability, jumpiness and randomness make it very difficult to accurately predict drought

[0003] Under the influence of climate change and human activities, there is a high degree of variability in the rainfall process, which poses great challenges to the practicability of existing prediction models and methods

The nonlinearity and instability of rainfall series make it impossible to fully capture the nonlinear factors in rainfall series by using a single model for forecasting rainfall series

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