基于云计算的数据挖掘技术在天气预报中的应用

发布时间：2023-05-20 01:15

　　气象资料是关系到国计民生的重要基础资源,社会经济发展、可持续发展等各个领域都需要气象工作提供可靠保障。随着气象现代化的进步和发展,气象行业积累了大量的数据,从海量的气象数据中挖掘出有用的信息,对于气象预测、预报和灾害预警都扮演着至关重要的作用。随着人们对气象预报精度要求的逐渐提高,提高预报准确率成为气象部门的工作重点,要提高数值天气预报准确率就必须解决计算量大和计算实时性问题,这就对气象部门的硬件设施和专业人才提出了很高的要求,同时带来了硬件成本的急剧增加。近年来,国内外大气科学领域学者基于数据挖掘方法的预报技术,即人工神经网络、遗传算法、支持向量机、贝叶斯、决策树和关联规则挖掘等方法开展了大量研究,并取得了显著成果,天气预报的准确率在不断提高,但是与人们的期望还有距离。因此,如何充分有效的发挥数据挖掘在天气预报中的重要作用,满足人们的需求至关重要。随着气象数据规模飞速增长,BP神经网络由于其强大的非线性系统拟合能力,在气象数据的分析和预测中得到广泛应用。因此,本研采用BP神经网络方法,以降水量和气温作为输入因子,建立天气预报模型。对降水天气的研究,本研究对所选的样本进行4种方式的组合...

【文章页数】：53 页

【学位级别】：硕士

【文章目录】：
摘要
Abstract
1 Introduction
    1.1 Research Background
    1.2 Research Objective and Significance
        1.2.1 Research Objective
        1.2.2 Research Significance
    1.3 Domestic and O verseas Progress
        1.3.1 The Present Situation of C loud Computing
        1.3.2 The C urrent Situation of Data Mining
        1.3.3 Application of Data Mining Method in Weather Forecast
    1.4 Main Content and Research Methods
        1.4.1 Main Content
        1.4.2 Research Methods
2 Theoretical and Model Analysis
    2.1 The Basic Introduction of Data Mining
        2.1.1 Data Mining Definition
        2.1.2 The Data Mining Process
        2.1.3 Methods of Data Mining
    2.2 Artificial Neural Network
        2.2.1 Overview of the Artificial Neural Network
        2.2.2 BP Algorithm Description
        2.2.3 BP Neural Network Process
    2.3 Summary
3 Set up Weather Forecasts model base on ANN
    3.1 Weather Forecast Analysis and Comparison
        3.1.1 Weather Forecast
        3.1.2 Main Methods of Weather Forecast
    3.2 Application of BP Neural Networks in Weather Forecasting
        3.2.1 Data Sources
        3.2.2 Basic Condition of Sample
    3.3 Set up Forecasting Model
        3.3.1 Perceptron Model
        3.3.2 Construction of the Theoretical Model
4 BP Structure Build
    4.1 Application of BP Neural Networks in Rainfall
        4.1.1 Data Preprocessing
        4.1.2 Training Sample Selection
    4.2 Application of BP Neural Networks in Temperature
        4.2.1 BP Neural Network Design
        4.2.2 Pretreatment of Input and O utput Data
        4.2.3 Simulation of Temperature Sequence
    4.3 BP Building
    4.4 Conclusion
5 Experimenting the Performance
    5.1 Simulation Environment
    5.2 Simulation Result
        5.2.1 Forecasting Model
        5.2.2 Input Data
        5.2.3 Simulation Result
Conclusion
References
Research Projects and Publications in Master Study
Acknowledgement



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