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



$ 100.001-250.000


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"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

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


Información importante

¿Qué objetivos tiene esta formación?: "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"

¿Esta formación es para mí?: "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..]"

Requisitos: "No prior knowledge in programming needed Basic knowledge in R studio environment Basic knowledge in GIS and QGIS is optional"

Sedes y fechas disponibles





Fechas disponibles Inscripciones abiertas


  • Excel
  • Testing
  • Network
  • Export
  • Import
  • MS Excel
  • Network Training
  • GIS
  • Model
  • Map
  • QGIS
  • Spatial

Programa académico

"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"

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

$ 100.001-250.000