Descriptions
Prediction Mapping Using GIS Data and Advanced ML Algorithms, This course applies advanced machine learning classification techniques with remote sensing and geospatial data to create prediction maps for environmental and hazard-related applications. Project 1 focuses on multi-label classification problems, such as predicting species distribution, air pollution limits, and disease risks, with applications including PM10 susceptibility mapping published in scientific research. Project 2 emphasizes binary classification problems, such as landslide susceptibility mapping, flood prediction, climate change impacts, and oil spill detection.
Through a step-by-step approach, learners implement algorithms like XGBoost, KNN, Naïve Bayes, and Random Forest, optimizing hyperparameters, validating results with metrics such as confusion matrices and AUC scores, and generating prediction maps in raster and vector formats. By the end, participants will have practical experience applying machine learning in GIS contexts and producing professional-quality prediction outputs for research and decision-making.
What you’ll learn
- Create prediction maps for landslides and air pollution
- Implement ML algorithms: XGBoost, KNN, Naïve Bayes, RF
- Optimize hyperparameters for classification tasks
- Validate models with confusion matrices and AUC scores
- Generate raster and vector prediction maps
- Apply machine learning in GIS for research and decision-making
Who this course is for
- Students, researchers, and professionals using GIS data mining
- Health researchers analyzing susceptibility maps
- Hazard researchers studying floods, landslides, droughts, and pollution
Specificatoin of Prediction Mapping Using GIS Data and Advanced ML Algorithms
- Publisher : Udemy
- Teacher : Dr. Omar AlThuwaynee
- Language : English
- Level : Intermediate
- Number of Course : 70
- Duration : 15 hours and 49 minutes
Content of Prediction Mapping Using GIS Data and Advanced ML Algorithms
Requirements
- No prior knowledge in programming needed
- Basic knowledge in R studio environment
- Basic knowledge in GIS and QGIS
- Basic knowledge about man made and natural hazards
Pictures
Sample Clip
Installation Guide
Extract the files and watch with your favorite player
Subtitle : English
Quality: 720
Download Links
Password file(s): www.downloadly.ir
File size
7.32 GB
