How To Easily Use Ann For Prediction Mapping Using Gis Data?

[mitsumi]

Last updated 2/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 7.03 GB | Duration: 7h 16m

First Simplified Step-by-Step Artificial Neural Network Methodology in R for Prediction Mapping using GIS Data

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
Requirements
No prior knowledge in programming needed
Basic knowledge in R studio environment
Basic knowledge in GIS and QGIS is optional
Description
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
Overview
Section 1: Introduction
Lecture 1 Course outlines
Lecture 2 Expected Outcomes
Section 2: ANN basic background and used packages
Lecture 3 Introduction to ANN and used functions
Lecture 4 Introduction to NuralNet package
Lecture 5 Introduction Summary
Section 3: Create training and testing data in QGIS work environment
Lecture 6 Adding my developed Model tools to QGIS (version 3.14) processing library
Lecture 7 Create Land Cover map (convert string observations to numeric) in QGIS
Lecture 8 Run the tools Step 1
Lecture 9 Run the tools Step 2
Lecture 10 Run the tools Step 3
Section 4: Manage training and testing data in Excel
Lecture 11 Excel work step 1
Lecture 12 Excel work step 2
Section 5: Introduction to code settings and data processıng in R studio environment
Lecture 13 Outlines of the code contents
Lecture 14 Working directory settings and data input
Lecture 15 Convert Slope Aspect Categorical data into Numeric
Lecture 16 Convert Land-cover Categorical data into Numeric
Lecture 17 Data Scaling
Lecture 18 Testing Data processing
Section 6: Run ANN NeuralNet (nn) package and get results plots
Lecture 19 Run NeuralNet (nn) function
Lecture 20 Plot NeuralNet (nn) and get error estimation
Lecture 21 Adding NN function prediction output to training data frame
Lecture 22 How to convert values from scaled to original dataframe
Lecture 23 Pairwise plot of training dataframe and function output
Lecture 24 Generalized weight (GW) plot of training dataframe and function output
Section 7: (optional) Run NNET package and plot outputs
Lecture 25 Run NNET function and get variables importance plot
Lecture 26 Plot NNET function network
Lecture 27 Run Sensitivity test using NNET function
Section 8: Prediction map processing using NeuralNet (nn) function
Lecture 28 Run compute function (prediction function) and get cross tabulation results
Lecture 29 Update dataframe and run the previous step again
Lecture 30 Get cross tabulation for updated dataframe prediction
Lecture 31 Run compute function (prediction) with testing data and get cross tabulation
Lecture 32 Run ROC for function success and prediction rate results
Section 9: Final Prediction map production and visualization using NeuralNet
Lecture 33 Import raster files into R studio
Lecture 34 Rasters processing (extents, resampling and stacking)
Lecture 35 Scale Rasters stack data
Lecture 36 Run compute (prediction) function for Rasters stack data
Lecture 37 Produce final prediction Raster map
Lecture 38 Export prediction raster map to QGIS
Section 10: Code Conclusion and Summary
Lecture 39 Code Conclusion and Summary
All students, researchers and professionals that interested in using data mining with GIS Data,All students, researchers and professionals that work on: Health[viruses susceptibility, noise maps, Epidemic expansions, Infectious Disease, Famine ],All students, researchers and professionals that work on: Hazards[ flooding, landslides, geological based, drought, air pollution..]

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