Integration of Seasonal ARIMA in the GR4J Conceptual Model f

发布时间：2023-04-12 03:41

　　

【文章页数】：110 页

【学位级别】：硕士

【文章目录】：
Acknowledgement
Abstract
Chapter 1 Introduction
    1.1 Introduction
    1.2 Description of the conceptual models
        1.2.1 Purpose of the rainfall-runoff modeling
        1.2.2 The production function and transfer function
        1.2.3 The effectiveness of a rainfall-runoff model
        1.2.4 Definition of the conceptual model
        1.2.5 Presentation of GR models
    1.3 Seasonal ARIMA Model
        1.3.1 The Research Problem
        1.3.2 The Importance of the Research
        1.3.3 Research Methodology of SARIMA
    1.4 Technical route
Chapter 2 Description of the study area
    2.1 Introduction
    2.2 Construction of digital drainage basin
        2.2.0 DEM
        2.2.1 DEM pretreatment
        2.2.2 Extraction of water flow direction
        2.2.3 Extraction of river network in the drainage basin
        2.2.4 Physiographic condition
    2.3 Conclusion
Chapter 3 Daily rainfall-runoff model GR4J
    3.1 Introduction:
    3.2 Definition
    3.3 The parameters of GR4J
    3.4 The structure of GR4J
    3.5 Mathematical description
        3.5.1 Neutralization
        3.5.2 Performance function
        3.5.3 Percolation
        3.5.4 Unit hydrographs
        3.5.5 Exchange function with non-atmospheric exterior
        3.5.6 Routing tank
        3.5.7 Total flow
    3.6 Quality criteria of the model
    3.7 Conclusion
Chapter 4 Rainfall-Runoff Modeling Using GR4J Model in Source:A Pilot Study inLijiang Basin
    4.1 Introduction
    4.2 Data input
        4.2.1 DEM
        4.2.2 Temporal data
    4.3 Modeling of daily flows by the GR4J model
        4.3.1 Variants linked to calibration and validation periods
        4.3.2 Calibration and validation results
        4.3.3 Application period
    4.4 Final results
    4.5 Conclusion
Chapter 5 Time Series SARIMA Model
    5.1 Introduction
    5.2 Fundamental Concepts
        5.2.1 Deterministic and Stochastic Time Series
        5.2.2 Residuals (Application Errors)
        5.2.3 White Noise Series or White Noise Process
    5.3 Objectives of Time Series Analysis
    5.4 Component of Time Series
        5.4.1 Secular Trend
        5.4.2 Seasonal Variation
    5.5 Time series models
        5.5.1 Model introduction
        5.5.2 The Box and Jenkins Approach
        5.5.3 Testing Stationarity of Time Series
        5.5.4 Differencing
        5.5.5 SARIMA model
        5.5.6 Diagnostic Check of the Residuals and Model Adequacy
        5.5.7 ACF and PACF Plots of the Residuals
        5.5.8 Ljung-Box Chi-Square Test
        5.5.9 Measuring Accuracy
    5.6 Models Forecasts
    5.7 Conclusion
Chapter 6 Daily rainfall forecast of Lijiang catchment using SARIMA
    6.1 Introduction
    6.2 Data Description
    6.3 Preliminary Investigation of the Data
    6.4 Model Identification
    6.5 Parameters Estimation
    6.6 Diagnostic Tests
        6.6.1 Analysis of residuals
    6.7 Forecasting
Chapter 7 Conclusion and recommendations
    7.1 Conclusion
    7.2 Recommendations
References



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