利用EICM“增强集成气候模型”预测温度梯度

发布时间：2022-12-23 19:39

　　在考虑分析技术的同时,分析被认为是必要的。人行道暴露的无数气候情况影响路面变形机制及其性能。路面设计中气候方面的融合对于机械-经验设计实践的发展具有重要意义。为了考虑路面设计中的气候不一致性,必须在较长时间内改进包括气候区的临界路面温度和降水的数据库。在近似连续函数的同时,可以采用不同的方法。保持使用该方法的准确度。从泰勒学院的系列开始,转向Guass-Jacobian方法。每种方法都有其实用性。在本研究中,将用于python语言的Runge Kutta方法的算法应用于用于预测路面温度的数据集。假设系列是连续的,并且可以在将其移动到曲线上时预测其值。首先,制定了一个合适的函数,并将其应用于数据,以便对特定日期进行预测。在时间序列分析期间,在给定的参考术语中使用具有适当精确度的图表在校准和数据解释中起重要作用。路面设计中的气候变量组合对于开发机械-经验设计叙事非常重要。由于气候实体的变化而评估变化是非常重要的以及准确度。本文包含了从多项式的角度进行分析的所有算法,从线性扩展到四次。论文介绍了使用图形用户界面生成图形的算法。 

【文章页数】：82 页

【学位级别】：硕士

【文章目录】：
摘要
Abstract
Chapter1-Historical Background
    1.0 MERRA-Modern-Era Retrospective Analysis for Research and Application
        1.0.1 Horizontal Structure
        1.0.2 Vertical Structure
        1.0.3 MERRA-2 data collections
        1.0.4 Conventional Observations
        1.0.5 Satellite Observations of Wind
    1.1 Elucidation of EICM
    1.2 Research Background for the Enhanced Integrated Climatic Model(EICM)
    1.3 Input Parameters Needed to Run and Evaluate The EICM
    1.4 Climate and Pavement Distress
        1.4.1 Solar Radiation
        1.4.2 Thermal Radiation
        1.4.3 Blackbody Radiation
            1.4.3.1 Planck's Law
            1.4.3.2 Angular Dependence of Radiation
    1.5 Depth
    1.6 Heat Conductivity
    1.7 Reconnaissance and Data Collection
    1.8 Air Temperature Instrumentation Data
    1.9 Asphalt Temperature Instrumentation Data
    1.10 Surface Temperature
    1.11 Time of Temperature Measurements
    1.12 Temperature Depth Data
    1.13 Thermistor Depths
    1.14 Temperature Hole Depths
    1.15 LTPP Website
    1.16 Data Selection link
    1.17 Data Bucket Selection
    1.18 Data Introduction
    1.19 Data Analysis Selection
    1.20 Problem Statement EICM-P
Chapter2-Methodology
    2.1 Linear Regression
    2.2 Single Variate Linear Regression
    2.3 Multivariate Linear Regression
    2.4 Specifying the Model
    2.5 Normalization of Data
        2.5.1 Min-max normalization
        2.5.2 Z-score normalization
        2.5.3 Normalization by decimal scaling
    2.6 R-squared
    2.7 Root Mean Square Error(RMSE)
    2.8 Mean Squared Error
    2.9 Runge-Kutta Methods of Order Two
        2.9.1 Midpoint Method
        2.9.2 Modified Euler Method
        2.9.3 Higher-Order Runge-Kutta Methods
    2.10 PREDICTION MODELS
    2.11 Shading Effect on Infrared Measurements
    2.12 MATLAB Analysis
    2.13 K-Fold Cross Validation
    2.14 Selection of EICM-P
    2.15 Selection of Python
    2.16 Why Coding was necessary?
    2.17 Layer Nomenclature
    2.18 Brief History of GUI…..
    2.19 GUI Development
    2.20 Graphs of the output Results
        2.20.1 Linear Regression Graphs
        2.20.2 Combined Model Summary of all Layers
        2.20.3 Runge-Kutta Graphs
        2.20.4 Histograms
        2.20.5 Temperature Gradients
        2.20.6 Polynomials Fittings
    2.21 10-year Comparison
Chapter3 Deciphering the Literature
    3.1 EICM Paraphernalia
    3.2 Heat Conduction Module for Road Material
    3.3 Linear Model for Maximum Pavement Temperature
    3.4 Conduction heat transfer
Chapter4-Elucidation of Research Findings
Chapter5 Conclusion Summary
Recommendations for Future Work
Acknowledgements
References
List of Figures
List of Tables
Author’s Bibliography



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