Rating 3.75 out of 5 (134 ratings in Udemy)
What you'll learn- 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 …
Rating 3.75 out of 5 (134 ratings in Udemy)
What you'll learn- 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
DescriptionArtificial 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
