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First Simplified Step-by-Step Artificial Neural Network Methodology in R for Prediction Mapping using GIS Data
An excellent training about Data Science
How to easily use ANN for prediction mapping using GIS data?
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 dataPlot NN function networkPairwise NN model results of Explanatories and Response DataGeneralized Weights plot of Explanatories and Response DataVariables importance using NNET Package functionRun NNET functionPlot NNET function networkVariables importance using NNETSensitivity analysis of Explanatories and Response DataRun Neural net function for prediction with validation dataPrediction Validation results with AUC value and ROC plotProduce prediction map using Raster dataImport and process thematic maps like, resampling, stacking, categorical to numeric conversion. Run the compute (prediction function)Export final prediction map as raster. tif
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