Rainfall prediction method based on improved decision tree algorithm

Abstract

The invention relates to a rainfall prediction method based on an improved decision tree algorithm. The method comprises the steps that collecting meteorological data of various regions in several years and corresponding grade data; normalizing meteorological data to obtain a corresponding normalized data set, and dividing the normalized data set into a training set and a test set in proportion; substituting the training set into the improved decision tree network for training, substituting the test set into the trained decision tree model, checking a training result, finally inputting to-be-tested data into the trained decision tree network for prediction, outputting a result, and evaluating the rainfall grade of the to-be-tested data. An independent variable average influence value is adopted for selection by taking the decision tree as the core, selection is made according influence of attribute on the result, the attributed with high influence degree is selected for branching, theimproved decision tree algorithm is used for training, mass data is fully utilized, the prediction accuracy is improved, and misjudgment and judgment neglect are reduced.

Description

technical field [0001] The invention relates to a data mining technology, in particular to a precipitation prediction method based on an improved decision tree algorithm. Background technique [0002] With the development of society and economy and the continuous improvement of human requirements for meteorological services, the collection channels of meteorological data in the field of meteorology are becoming more and more abundant, and the scale of data is increasing, and its spatial attributes, high dimensionality, and instability are important for the study of traditional weather forecasting. It is extremely difficult to model, especially in the study of the internal relationship between various meteorological elements, which leads to the ineffective use of a large amount of meteorological data obtained, and has no substantive effect on promoting the development of meteorological model forecasts. The internal interaction conditions of the weather system are intricate. W...

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Problems solved by technology

[0002] With the development of society and economy and the continuous improvement of human requirements for meteorological services, the collection channels of meteorological data in the field of meteorology are becoming more and more abundant, and the scale of data is increasing, and its spatial attributes, high dimensionality, and instability are important for the study of traditional weather forecasting. The model increases the difficulty, especially when studying the internal relationship between various meteorological elements, which leads to the ineffective use of a large amount of meteorological data obtained, and has no substantial effect on promoting the development of meteorological model forecasts

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