GIS Prediction Mapping Using Artificial Neural Networks in R

Traditional regression models often fall short when dealing with complex, non-linear relationships in geographic data. Artificial Neural Networks (ANNs) offer a powerful, modern alternative for creating highly accurate spatial prediction and susceptibility maps. This text-based course guides you through the entire pipeline of spatial machine learning, from handling raw GIS data to exporting finished predictive maps. You will gain the practical skills to bridge the gap between geographic information systems (GIS) and advanced statistical modeling in R, using modern packages and workflows. What you'll learn: - Understand the foundational concepts of artificial neural networks and spatial prediction mapping. - Prepare and clean spatial raster and vector data using QGIS and modern R packages like terra. - Train neural network models in R to model complex, non-linear spatial relationships. - Evaluate model performance using sensitivity analysis, variable importance, and ROC/AUC metrics. - Apply spatial validation techniques to ensure model reliability and prevent overfitting. - Generate and export final predictive risk maps as GIS-ready raster files. You will start by mastering foundational spatial concepts and data preparation workflows. From there, the course walks you through configuring, training, and validating neural network models, concluding with the generation and export of professional-grade predictive rasters. This course is designed for beginners in spatial data science, GIS analysts, and environmental researchers who want to expand their predictive modeling toolkit. No prior experience with neural networks is required. Start building smarter, data-driven spatial predictions today.