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4.65
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How to easily use ANN for prediction mapping using GIS data?

First Simplified Step-by-Step Artificial Neural Network Methodology in R for Prediction Mapping using GIS Data
Instructor:
Dr. Omar AlThuwaynee
278 students enrolled
English [Auto]
With Step by step description we will be together facing the common software and code misleadings.
1. Produce training and testing data using automated tools in QGIS (Optional). Or jump this and using your own training/testing data directly.
2. Run NeuralNet function with training data and testing data. (use my QGIS tools as an option OR use your preferable data production technique directly)
3. Plot NN function network and get all the outputs like; Error rate, statistics, Pairwise and Generalized weight plot
4- Prediction and Validation Mapping Accuracy using AUC value of ROC plot
4. Produce and export prediction map using Raster 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.

  1. Produce training and testing data using automated tools in QGIS OR SKIP THIS STEP AND USE YOUR OWN TRAINING AND TESTING DATA

  2. Run Neural net function with training data and testing data

    1. Plot NN function network

    2. Pairwise NN model results of Explanatories and Response Data

    3. Generalized Weights plot of Explanatories and Response Data

  3. Variables importance using NNET Package function

    1. Run NNET function

    2. Plot NNET function network

    3. Variables importance using NNET

    4. Sensitivity analysis of Explanatories and Response Data

  4. Run Neural net function for prediction with validation data

    1. Prediction Validation results with AUC value and ROC plot

  5. Produce prediction map using Raster data

    1. Import and process thematic maps like, resampling, stacking, categorical to numeric conversion.

    2. Run the compute (prediction function)

    3. Export final prediction map as raster.tif

You can view and review the lecture materials indefinitely, like an on-demand channel.
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