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
- Description
- Curriculum
- FAQ
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In this course, four machine learning supervised classification based techniques used with remote sensing and geospatial resources data to predict two different types of applications:
Project 1: Data of Multi-labeled target prediction via multi-label classification (multi class problem). Target (Y) that has 3 labeled classes (instead of Numbers): Names, description, ordinal value (small, large, X-large)..Multiple output maps. Like:
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Increase specific type of species in certain areas and its relationship with surrounding conditions.
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Air pollution limits prediction (Good, moderate, unhealthy, Hazardous..)
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Complex diseases types: potential risk factors and their effects on the disease are investigated to identify risk factors that can be used to develop prevention or intervention strategies.
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Course application: Prediction of concentration of particulate matter of less than 10 µm diameter (PM10)
Project 2: Data of Binary labeled target prediction. Target with 2 classes: Yes and No, Slides and No slide, Happened –Not happened, Contaminated- Clean.
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Flooded areas and it contribution factors like topographic and climate data.
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Climate change related consequences and its dragging factors like urban heat islands and it relationship with land uses.
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Oil spills: polluted and non polluted.
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Course application:Landslide susceptibility mapping in prone area.
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If you are previously enrolled in my previous course using ANN, then you have the chance to compare the outcomes, as we used the same landslide data here.
Eventually, all the measured data (training and testing), were used to produce the prediction map to be used in further GIS analysis or directly to be presented to decision makers or writing research article in SCI journals.
This course considered the most advanced, in terms of analysis models and output maps that successfully invested in the (1) machine learning algorithm and geospatial domains; (2) free available data of remote sensing in data scarce environment.
Practical summary about the classification based machine learning algorithms!
Project 1: PM10 prediction mapping : Data record pre-processing and data entry
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5
CARET package in R -
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Hyperparameters optimization (model tuning) in machine learning
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eXtreeme Gradient Boosting (XGBoost) classifier machine learning
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K- nearest neighbors (KNN) classifier machine learning
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Naive Bayes (NB) classifier machine learning
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Ensemble Random forest (RF) classifier machine learning
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Selection of training and testing data concept
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Current computer and software's specifications that used in the course
Project 1 PM10 prediction mapping : Input data-frame processing and production
Project 1 PM10 prediction mapping : modeling of advanced ML classifiers in R
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Preparation of PM10 prediction remote sensing variables data-list
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Landsat 8 imagery download
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Visualization of downloaded Landsat 8 images
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Processing of Landsat 8 bands and indices in R
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Processing of Land Surface Temperature (LST) in R
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Processing of average monthly and annual Landsat 8 bands and indices in R
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Processing and production of road networks variable in QGIS
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Preparation of input dataframe (target and conditioning factors) in QGIS
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Finalize input variables and convert it to CSV format file in QGIS for modeling
Project 2 Landslide : Create training and testing data in QGIS
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XGBoost algorithm: Data entry and visualization in R
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XGBoost algorithm: Run of train default function
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XGBoost algorithm: Hyper-parameter optimization and plot (model tuning)
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XGBoost algorithm: AUC value of ROC plot
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XGBoost algorithm: Fit optimized model using all inventory observations
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XGBoost algorithm: Conversion to dataframe and scaling of Raster images
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XGBoost algorithm: Probability prediction maps production
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XGBoost algorithm: Classification prediction maps production
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NB algorithm: ggplot of linearity between target and independents and variables
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NB algorithm: Run of train default function
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NB algorithm: Hyper-parameter optimization, AUC of ROC plot & normalized Rasters
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NB algorithm: Probability and classification prediction maps production
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KNN algorithm: Run of train function and hyper-parameter optimized models
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KNN algorithm: AUC of ROC and probability and classification prediction maps
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RF algorithm: Data entry and train function using Grid search tuning
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RF algorithm: train function using Random search tuning and AUC of ROC
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RF algorithm: Scaling and conversion of raster images to dataframe
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RF algorithm: Probability prediction map
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RF algorithm: Classification prediction map
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Summary and Visualization of 4 algorithms prediction resultant maps in QGIS
Project 2 Landslide prediction mapping : pre-processing training data in Excel
Project 2 Landslide prediction mapping : modeling of advanced ML classifiers
Projects Conclusion and main remarks of the presented course
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XGBoost algorithm : Training and testing data entry in R
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XGBoost algorithm : Run train function using default settings
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XGBoost algorithm: Hyper-parameter optimization (model tuning) and pairs plot
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XGBoost algorithm: AUC of ROC plot and important technical error
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XGBoost algorithm: Run optimized model and probability prediction maps
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XGBoost algorithm: Classification prediction map production
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KNN algorithm : Data entry and visualization of target and other variables
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KNN algorithm: Run of train function and hyper-parameter optimized models
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KNN algorithm: AUC of ROC plot and technical issues with data entry
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KNN algorithm: probability prediction maps
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KNN algorithm: classification prediction map
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NB algorithm: Training data entry and visualization of variables
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NB algorithm: Train function and Hyper-parameters and AUC of ROC plot
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NB algorithm: Probability and classification prediction maps production
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RF algorithm: Data entry of training data variables
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RF algorithm: default train function and Hyper-parameter and AUC of ROC plot
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RF algorithm: Probability and classification prediction maps
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Summary and Visualization of 4 algorithms prediction maps in QGIS
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!
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Course details
Video
16 hours
Certificate of Completion
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