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

How to easily use ANN for prediction mapping using GIS data?, available at $59.99, has an average rating of 4.85, with 39 lectures, based on 176 reviews, and has 922 subscribers.

You will learn about 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 This course is ideal for individuals who are All students, researchers and professionals that interested in using data mining with GIS Data or All students, researchers and professionals that work on: Health [viruses susceptibility, noise maps, Epidemic expansions, Infectious Disease, Famine ] or All students, researchers and professionals that work on: Hazards [ flooding, landslides, geological based, drought, air pollution..] It is particularly useful for All students, researchers and professionals that interested in using data mining with GIS Data or All students, researchers and professionals that work on: Health [viruses susceptibility, noise maps, Epidemic expansions, Infectious Disease, Famine ] or All students, researchers and professionals that work on: Hazards [ flooding, landslides, geological based, drought, air pollution..].

Enroll now: How to easily use ANN for prediction mapping using GIS data?

Summary

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

Price: $59.99

Average Rating: 4.85

Number of Lectures: 39

Number of Published Lectures: 39

Number of Curriculum Items: 39

Number of Published Curriculum Objects: 39

Original Price: $139.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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..]

Target Audiences

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..]

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 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
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
Run Neural net function for prediction with validation data
1. Prediction Validation results with AUC value and ROC plot
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

IMPORTANT: LaGriSU Version 2023_03_09 is available (Free) to download using Github link (please search for /Althuwaynee/LaGriSU_Landslide-Grid-and-Slope-Units-QGIS_ToolPack)

*LaGriSU (automatic extraction of training / testing thematic data using Grid and Slope units)

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course outlines

Lecture 2: Expected Outcomes

Chapter 2: ANN basic background and used packages

Lecture 1: Introduction to ANN and used functions

Lecture 2: Introduction to NuralNet package

Lecture 3: Introduction Summary

Chapter 3: Create training and testing data in QGIS work environment

Lecture 1: Adding my developed Model tools to QGIS (version 3.14) processing library

Lecture 2: Create Land Cover map (convert string observations to numeric) in QGIS

Lecture 3: Run the tools Step 1

Lecture 4: Run the tools Step 2

Lecture 5: Run the tools Step 3

Chapter 4: Manage training and testing data in Excel

Lecture 1: Excel work step 1

Lecture 2: Excel work step 2

Chapter 5: Introduction to code settings and data processıng in R studio environment

Lecture 1: Outlines of the code contents

Lecture 2: Working directory settings and data input

Lecture 3: Convert Slope Aspect Categorical data into Numeric

Lecture 4: Convert Land-cover Categorical data into Numeric

Lecture 5: Data Scaling

Lecture 6: Testing Data processing

Chapter 6: Run ANN NeuralNet (nn) package and get results plots

Lecture 1: Run NeuralNet (nn) function

Lecture 2: Plot NeuralNet (nn) and get error estimation

Lecture 3: Adding NN function prediction output to training data frame

Lecture 4: How to convert values from scaled to original dataframe

Lecture 5: Pairwise plot of training dataframe and function output

Lecture 6: Generalized weight (GW) plot of training dataframe and function output

Chapter 7: (optional) Run NNET package and plot outputs

Lecture 1: Run NNET function and get variables importance plot

Lecture 2: Plot NNET function network

Lecture 3: Run Sensitivity test using NNET function

Chapter 8: Prediction map processing using NeuralNet (nn) function

Lecture 1: Run compute function (prediction function) and get cross tabulation results

Lecture 2: Update dataframe and run the previous step again

Lecture 3: Get cross tabulation for updated dataframe prediction

Lecture 4: Run compute function (prediction) with testing data and get cross tabulation

Lecture 5: Run ROC for function success and prediction rate results

Chapter 9: Final Prediction map production and visualization using NeuralNet

Lecture 1: Import raster files into R studio

Lecture 2: Rasters processing (extents, resampling and stacking)

Lecture 3: Scale Rasters stack data

Lecture 4: Run compute (prediction) function for Rasters stack data

Lecture 5: Produce final prediction Raster map

Lecture 6: Export prediction raster map to QGIS

Chapter 10: Code Conclusion and Summary

Lecture 1: Code Conclusion and Summary

Instructors

Dr. Omar AlThuwaynee
PhD. of Civil and Geomatics Engineering

Rating Distribution

1 stars: 5 votes
2 stars: 4 votes
3 stars: 21 votes
4 stars: 52 votes
5 stars: 94 votes

Frequently Asked Questions

How long do I have access to the course materials?

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

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu