How to easily use ANN for prediction mapping using GIS data?

🌟 Course Title: First Simplified Step-by-Step Artificial Neural Network Methodology in R for Prediction Mapping using GIS Data

Headline: Master the Basics of ANN with Ease: A Beginner-Friendly Guide to Prediction Mapping in R Using GIS Datasets!

Course Description:

Embark on a journey into the fascinating world of Artificial Neural Networks (ANNs), a cornerstone of Artificial Intelligence (AI) that has made significant strides across diverse fields, including social sciences, medical research, and engineering applications. ANNs are celebrated for their high reliability and validity, which can be further fine-tuned through various settings, making them an invaluable tool for prediction mapping—a technique particularly favored by researchers for its accuracy compared to traditional regression or classification methods.

In this course, Dr. Omar AlThuwaynee will guide you through the process of integrating ANNs with Spatial data within R, utilizing the NeuralNet Package alongside Landslides datasets and thematic maps. This course is designed to be accessible, even if you're new to ANNs or GIS data analysis, providing you with all the necessary data, code, and resources to follow along and succeed.

Course Outline:

Here's what you can expect to learn through this course:

1. Data Preparation: 📊

   • Learn how to produce training and testing datasets using QGIS, or skip this step and use your own datasets.

2. Neural Network Modeling:

   • Execute the NeuralNet function with both training and testing data.
   • Visualize the ANN network structure.
   • Analyze pairwise results between explanatory variables and the response variable.
   • Interpret the generalized weights of the variables in relation to the response variable.

3. Variable Importance:

   • Utilize the NNET Package to identify critical variables affecting your response.
   • Plot the network structure for clarity.
   • Conduct a sensitivity analysis to understand variable impact.

4. Model Validation and Prediction:

   • Run the NeuralNet function to validate your model with validation data.
   • Obtain and interpret the AUC value and create a ROC plot to evaluate performance.

5. Prediction Mapping:

   • Learn to produce a prediction map using raster data.
   • Process thematic maps by resampling, stacking, and converting categorical data to numeric formats.
   • Apply the compute (prediction) function.
   • Export your final prediction map as raster.tif.

Important Note: 📢 To enhance your experience with this course, we recommend using LaGriSU—a powerful tool for automatic extraction of training and testing datasets from thematic maps using Grid and Slope Units (GSU). You can download the latest version (2023_03_09) of LaGriSU for free from Dr. Omar AlThuwaynee's Github repository.

Join us in this comprehensive course to unlock the potential of ANNs for prediction mapping with GIS data. Whether you're a student, professional, or an enthusiast, this course will equip you with the tools and knowledge to confidently apply AI in spatial analysis and beyond! 🚀📡🌍