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

AulaGEO Academy

Training

Online

up to £ 100

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Description

  • Type

    Training

  • Methodology

    Online

  • Class hours

    8h

  • Duration

    Flexible

  • Start date

    Different dates available

"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 network -Pairwise NN model results of Explanatories and Response Data -Generalized Weights plot of Explanatories and Response Data -Variables importance using NNET Package functionRun 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 dataPrediction Validation results with AUC value and ROC plot -Produce 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"

Facilities

Location

Start date

Online

Start date

Different dates availableEnrolment now open

About this course

"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 plot4- Prediction and Validation Mapping Accuracy using AUC value of ROC plot4.Produce and export prediction map using Raster data"

"All students, researchers and professionals that interested in using data mining with GIS DataAll 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..]"

"No prior knowledge in programming neededBasic knowledge in R studio environmentBasic knowledge in GIS and QGIS is optional"

"-100% online -Access to the course for life -30 days warranty money back -Available from desktop or mobile app -Can begin and finish the course any time -Can repeat the course any times"

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Reviews

This centre's achievements

2020

All courses are up to date

The average rating is higher than 3.7

More than 50 reviews in the last 12 months

This centre has featured on Emagister for 3 years

Subjects

  • Network Training
  • QGIS
  • GIS
  • model
  • GIS Data
  • GIS Training
  • ArcGIS Pro
  • Spatial analysis
  • Geospatial
  • Geographic
  • Geography
  • QGIS Training
  • QGIS tutorial
  • GIS Technology
  • Databases
  • Geodatabase
  • NNET
  • ANN NeuralNet
  • Excel
  • Excel project

Teachers and trainers (1)

AulaGEO Academy

AulaGEO Academy

Specialized center in Geospatial, Engineering and Operations

We choose the best courses and make them available to new audiences in the spectrum CAD - GIS - BIM - Digital Twins Our training offer covers the entire spectrum of data intelligence: Capture - Modeling - Design - Construction - Operation. The creators of courses with which we have decided to work or promote have been carefully selected, to offer a complementary set of knowledge. We firmly believe that today people do not seek courses to fill their walls with diplomas; but to make their abilities more productive.

Course programme

"Introduction
Course outlines
Expected Outcomes
ANN basic background and used packages
Introduction to ANN and used functions
Introduction to NuralNet package
Introduction Summary
Create training and testing data in QGIS work environment
Adding my developed tools to QGIS processing library
Create Land Cover map (convert string observations to numeric) in QGIS
Run the tools Step 1
Run the tools Step 2
Run the tools Step 3
Manage training and testing data in Excel
Excel work step 1
Excel work step 2
Introduction to code settings and data processing in R studio environment
Outlines of the code contents
Working directory settings and data input
Convert Slope Aspect Categorical data into Numeric
Convert Land-cover Categorical data into Numeric
Data Scaling
Testing Data processing
Run ANN NeuralNet (nn) package and get results plots
Run NeuralNet (nn) function
Plot NeuralNet (nn) and get error estimation
Adding NN function prediction output to training data frame
How to convert values from scaled to original dataframe
Pairwise plot of training dataframe and function output
Generalized weight (GW) plot of training dataframe and function output
(optional) Run NNET package and plot outputs
Run NNET function and get variables importance plot
Plot NNET function network
Run Sensitivity test using NNET function
Prediction map processing using NeuralNet (nn) function
Run compute function (prediction function) and get cross tabulation results
Update dataframe and run the previous step again
Get cross tabulation for updated dataframe prediction
Run compute function (prediction) with testing data and get cross tabulation
Run ROC for function success and prediction rate results
Final Prediction map production and visualization using NeuralNet
Import raster files into R studio
Rasters processing (extents, resampling and stacking)
Scale Rasters stack data
Run compute (prediction) function for Rasters stack data
Produce final prediction Raster map
Export prediction raster map to QGIS
Code Conclusion and Summary
Code Conclusion and Summary"

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