Climate change prediction method and system based on improved bp neural network fitting multiple climate models

Abstract

The present invention discloses a climate change prediction method and system for fitting various climate modes based on a modified BP neural network. The method comprises the following steps: collecting rainfall data that characterizes a climate change feature; performing downscaling processing on climate mode prediction grid data, so as to obtain a long series of data with the same resolution as measured data; performing average correction on a downscaled climate mode prediction series; establishing a modified BP neural network model and determining a network model structure; training and inspecting the established BP neural network model; and predicting a future climate spatial-temporal change using a model output. According to the method and system disclosed by the present invention, output / input data processing of the BP neural network is modified, and various climate modes are fit to predict the future climate change, so that reliability of the prediction result is increased, and the defects that the deviation of a single mode is relatively large during an assessment and that basin space nonuniformity is not considered during a multi-mode collection assessment and the like are made up.

Description

technical field [0001] The invention belongs to the application field of climatology and hydrology and water resources, in particular to a method for predicting future climate change by using a climate model. Background technique [0002] Future climate prediction under the background of climate change is one of the major scientific issues of global climate change. Objectively evaluating the future climate change trend, strengthening the adaptive management of water resources, and avoiding disadvantages are the major strategic needs of the country to deal with climate change. Global climate models are considered the primary tool for understanding and attributing past climate change and making projections for the future. Therefore, it is very important to choose a model with high performance. Whether the model can reasonably simulate the past climate change will directly affect whether the model can evaluate the "reproducibility" of the current interdecadal climate change, w...

AI Technical Summary

Problems solved by technology

However, when a single model evaluates the climate change trend of a specific watershed, there is often a large deviation; when using a multi-model set, using the same set of weight values ​​for a watershed does not take into account the spatial inhomogeneity of the watershed, and cannot correctly reflect the temporal and spatial distribution of climate change.

[0004] In addition, when BP neural network is used for forecasting, the forecast sequence value cannot be extended, which limits the application of BP neural network in climate prediction

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