"Artificial Neural Network (ANN) is one of the advanced Artificial Intelligence (AI) component, through many applications, vary from social, medical and applied engineering, ANN proves high reliability and validity enhanced by multiple setting options.
Using ANN with Spatial data, increases the confidence in the obtained results, especially when it compare to regression or classification based techniques. as called by many researchers and academician especially in prediction mapping applications.
Together, step by step with ""school-bus"" speed, will cover the following points comprehensively (data, code and other materials are provided) using NeuralNet Package in R and Landslides data and thematics maps.
Produce training and testing data using automated tools in QGIS OR SKIP THIS STEP AND USE YOUR OWN TRAINING AND TESTING DATA
-Run Neural net function with training data and testing data
Plot NN function network
-Pairwise NN model results of Explanatories and Response Data
-Generalized Weights plot of Explanatories and Response Data
-Variables importance using NNET Package function
Run NNET function
-Plot NNET function network
-Variables importance using NNET
-Sensitivity analysis of Explanatories and Response Data
-Run Neural net function for prediction with validation data
Prediction Validation results with AUC value and ROC plot
-Produce prediction map using Raster data
Import and process thematic maps like, resampling, stacking, categorical to numeric conversion.
-Run the compute (prediction function)
-Export final prediction map as raster.tif