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

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