Course on Complete Geostatistical Modeling

Course on Complete Geostatistical Modeling free download

Step-by-Step Guide to Spatial Data Analysis and Kriging

This course is designed to help students, researchers, and professionals understand and apply geostatistical methods for groundwater modeling. It covers the entire workflow—from data preparation to final visualization—using simple, practical steps.

Through this course, learners will gain essential skills in data analysis, spatial modeling, and geostatistics, which are important for groundwater assessment and environmental planning. Whether you're a beginner or someone with basic GIS knowledge, this course will guide you through each stage with clear explanations and examples.

What You Will Learn:

1. Groundwater Data Cleaning and Preparation:
   Learn how to clean, organize, and prepare groundwater datasets using R programming. You will also convert spatial coordinates to UTM format for accurate spatial analysis.

2. Exploratory Data Analysis (EDA):
   Use plots and summary statistics in R (such as histograms, box plots, and scatter plots) to explore patterns, trends, and outliers in groundwater data.

3. Normality Check and Data Transformation:
   Understand the importance of data normality in modeling. Learn how to test for normality and apply transformations (e.g., log or z-score) to make data suitable for analysis.

4. Polynomial Trend Surface Analysis:
   Detect large-scale spatial trends using polynomial regression in R. Remove the trend, perform analysis on residuals, and then add the trend back for accurate final outputs.

5. Variogram and Semivariogram Modeling:
   Learn to construct variograms and semivariograms to study spatial correlation. Use this knowledge to understand how groundwater levels vary with distance and direction.

6. Grid Creation and Block Kriging:
   Prepare spatial grids (e.g., 1 km × 1 km) and perform block kriging in R to estimate groundwater levels and uncertainty across the study area.

7. Cross-Validation Techniques:
   Apply Point Kriging Cross Validation (PKCV) to evaluate model accuracy and choose the most reliable semivariogram model.

8. Statistical Modeling and Interpretation:
   Develop and apply statistical models (like regression and kriging) using R. Learn how to interpret model parameters and results, understand uncertainty, and extract meaningful insights.

9. Visualization and Interpretation:
   Use QGIS to create professional maps showing estimated groundwater levels and kriging variance. Learn how to interpret spatial patterns for research, planning, and decision-making.

10. Integration of R and QGIS:
    Combine the power of R for analysis and QGIS for mapping. Learn a complete, real-world geostatistical modeling workflow using both tools together.

Course Benefits:

• Easy to understand: Concepts explained in simple and clear language

• Practical focus: Real-world workflow followed by professionals

• Comprehensive learning: Covers all major steps in geostatistical groundwater modeling

• Decision-making support: Learn how to interpret maps and make informed decisions

• Job-ready skills: Useful for careers in hydrogeology, environmental science, GIS, and civil engineering