Prediction Mapping Using GIS Data and Advanced ML Algorithms
eXtreme Gradient Boosting, K Nearest Neighbour, Naïve Bayes, Random Forest for Prediction Geo-Hazards and Air pollution
4.16 (129 reviews)
954
students
16 hours
content
Sep 2023
last update
$44.99
regular price
What you will learn
Two applications of susceptibility prediction mapping in GIS, 1) Landslides prediction maps 2) Ambient air pollution prediction maps
Step by step analysis of ML algorithms for classification: eXtreme Gradient Boosting (XGBoost) K nearest neighbour (KNN) Naïve Bayes (NB) Random forest (RF)
Run classification based algorithms with training data model accuracy, Kappa index, variables importance, sensitivity analysis of explanatory and response data
Hyper-parameter optimization procedure and application
Model accuracy test and validation using; confusion matrix and results validation using AUC value under ROC plot
Produce prediction maps using Raster and vector dataset
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2733334
udemy ID
1/2/2020
course created date
3/22/2020
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