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What would you learn in How to easily use ANN for prediction mapping using GIS data course?

Artificial Neural Network (ANN) is one of the latest Artificial Intelligence (AI) components with a wide range of applications that differ from medical, social, and engineering applications. ANN is highly reliable and accurate, which is further enhanced by various settings options.

The use of ANN combined with Spatial data increases the certainty of the results, mainly when they are compared to classification or regression methods based on classification. As referred to by many academics and researchers, particularly in the field of prediction mapping.

Together, step-by-step in "school-bus" speed, we will go over the following topics in depth (data codes, data, and other material are also provided), making use of the NeuralNet Program with R and Landslides maps and data.

1. Make training and test data with automated tools like QGIS, or skip this step and use your training and testing data.

2. Run Neural net function with training data as well as testing data

   1. Plot Network function network

   2. Model results for Pairwise NN models of Explanatory Data and Response Data

   3. Generalized Weights Plot of Explanatory Data and Response Data

3. Variables are importance using the NNET Package function

   1. Run NET function

   2. Plot NNET function network

   3. Variables' importance using NNET

   4. Analysis of Sensitivity in Explanatory Data and Response Data

4. "Run" Neural net function to predict using validation data

   1. Prediction Validation results using AUC value as well as ROC chart

5. Produce prediction map using Raster data

   1. Thematic map import and processing convert categorical information to numbers, such as stacking, resampling, and resampling.

   2. Perform the computation (prediction function)

   3. Export the final prediction map in raster.tif

Course Content:

• We will face the most common software and code snares with detailed descriptions.
• 1. Make training and testing data using QGIS (Optional) with automated tools. Alternatively, you can use your training/testing data.
• 2. Run the NeuralNet function with training and testing data. (use the QGIS tools for a second option or use your preferred method for producing data directly)
• 3. You can plot the NN function network to get the results like Error rate Statistics, Pairwise and Generalized weight plot
• 4. Precision and Prediction Mapping by using AUC values of ROC plot
• 4. Create and export a predictions map by using Raster data

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