Open AccessThe Rainfall and Meteorological Data Mining Model Based on Multi-Dimensional Precipitation Time Series
Author(s) -
Sizhe Ding
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1871/1/012137
Subject(s) - series (stratigraphy) , precipitation , cluster analysis , piecewise , time series , data mining , time sequence , computer science , dimension (graph theory) , sequence (biology) , meteorology , mathematics , geology , geography , artificial intelligence , machine learning , paleontology , mathematical analysis , genetics , biology , pure mathematics
In order to study the relationship between rainfall and related meteorological elements in rainy weather, find out the changes of related meteorological elements before and after rainfall, a multi-dimensional time series data mining model is proposed. The model first performs dimension selection pre-processing on the time series of meteorological elements to remove irrelevant and redundant dimensions, then uses the proposed extreme slope piecewise linear fitting method to segment the time series, data compression and eigenvalue extraction, and finally uses k-means clustering algorithm to symbolize the processed multi-dimensional sequence, and uses rules to extract the rainfall weather model. Experimental results show that the model has good practical value.