وب سایت تخصصی شرکت فرین
دسته بندی دوره ها

Prediction Mapping Using GIS Data and Advanced ML Algorithms

سرفصل های دوره

eXtreme Gradient Boosting, K Nearest Neighbour, Naïve Bayes, Random Forest for Prediction Geo-Hazards and Air pollution


1 - Introduction and Course Content Get to know what will we talk about
  • 1 - Course contents
  • 2 - Course applications Landslide and Air pollution prediction analysis
  • 3 - Projects data study areas and applications extent
  • 4 - Expected outcomes What will we achieve together

  • 2 - Practical summary about the classification based machine learning algorithms
  • 5 - CARET package in R
  • 6 - Hyperparameters optimization model tuning in machine learning
  • 7 - eXtreeme Gradient Boosting XGBoost classifier machine learning
  • 8 - K nearest neighbors KNN classifier machine learning
  • 9 - Naive Bayes NB classifier machine learning
  • 10 - Ensemble Random forest RF classifier machine learning
  • 11 - Selection of training and testing data concept
  • 12 - Current computer and softwares specifications that used in the course

  • 3 - Project 1 PM10 prediction mapping Data record preprocessing and data entry
  • 13 - PM10 readings preprocessing and input data preparation in Excel
  • 13 - PM10-2014-2019.xlsx
  • 14 - Allocate the air monitoring stations and record data entry in QGIS
  • 14 - shapfiles-of-study-area-pm10.zip
  • 15 - PM10 readings conversion to WHO limits in QGIS

  • 4 - Project 1 PM10 prediction mapping Input dataframe processing and production
  • 16 - Preparation of PM10 prediction remote sensing variables datalist
  • 16 - kirkuk-order-12-scences.zip
  • 17 - Landsat 8 imagery download
  • 18 - Visualization of downloaded Landsat 8 images
  • 19 - Images-processing.zip
  • 19 - Processing of Landsat 8 bands and indices in R
  • 19 - pm10-library.zip
  • 20 - Processing of Land Surface Temperature LST in R
  • 20 - lst production and other indices using r code.zip
  • 21 - Processing of average monthly and annual Landsat 8 bands and indices in R
  • 22 - Processing and production of road networks variable in QGIS
  • 23 - Preparation of input dataframe target and conditioning factors in QGIS
  • 24 - Finalize input variables and convert it to CSV format file in QGIS for modeling

  • 5 - Project 1 PM10 prediction mapping modeling of advanced ML classifiers in R
  • 25 - XGBoost algorithm Data entry and visualization in R
  • 25 - stat-all-no-road.csv
  • 25 - stat-road.csv
  • 26 - PM10-analysis-Kirkuk-XGBoost.zip
  • 26 - XGBoost algorithm Run of train default function
  • 27 - XGBoost algorithm Hyperparameter optimization and plot model tuning
  • 28 - XGBoost algorithm AUC value of ROC plot
  • 29 - XGBoost algorithm Fit optimized model using all inventory observations
  • 30 - XGBoost algorithm Conversion to dataframe and scaling of Raster images
  • 31 - XGBoost algorithm Probability prediction maps production
  • 32 - Levels-key.zip
  • 32 - XGBoost algorithm Classification prediction maps production
  • 33 - NB algorithm ggplot of linearity between target and independents and variables
  • 33 - PM10-analysis-Kirkuk-Naive-Bayes.zip
  • 33 - stat-all-no-road.csv
  • 33 - stat-road.csv
  • 34 - NB algorithm Run of train default function
  • 35 - NB algorithm Hyperparameter optimization AUC of ROC plot & normalized Rasters
  • 36 - Levels-key.zip
  • 36 - NB algorithm Probability and classification prediction maps production
  • 37 - KNN algorithm Run of train function and hyperparameter optimized models
  • 37 - PM10-analysis-Kirkuk-KNN.zip
  • 37 - stat-all-no-road.csv
  • 37 - stat-road.csv
  • 38 - KNN algorithm AUC of ROC and probability and classification prediction maps
  • 38 - Levels-key.zip
  • 39 - PM10-Kirkuk-Random-forest.zip
  • 39 - RF algorithm Data entry and train function using Grid search tuning
  • 39 - stat-all-no-road.csv
  • 39 - stat-road.csv
  • 40 - RF algorithm train function using Random search tuning and AUC of ROC
  • 41 - RF algorithm Scaling and conversion of raster images to dataframe
  • 42 - RF algorithm Probability prediction map
  • 43 - Levels-key.zip
  • 43 - RF algorithm Classification prediction map
  • 44 - Summary and Visualization of 4 algorithms prediction resultant maps in QGIS

  • 6 - Project 2 Landslide Create training and testing data in QGIS
  • 45 - Adding my developed tools to QGIS processing library
  • 45 - QGIS-Models-Grid.zip
  • 46 - Create Land Cover map convert string observations to numeric in QGIS
  • 46 - LandCover-shapfile.zip
  • 47 - Landslides-incidents-shp.zip
  • 47 - Original-layers.zip
  • 47 - Run the tools Step 1
  • 48 - Run the tools Step 2
  • 49 - Run the tools Step 3

  • 7 - Project 2 Landslide prediction mapping preprocessing training data in Excel
  • 50 - Excel.zip
  • 50 - Excel work step 1
  • 51 - Excel work step 2

  • 8 - Project 2 Landslide prediction mapping modeling of advanced ML classifiers
  • 52 - LS-XGBoost1.zip
  • 52 - XGBoost algorithm Training and testing data entry in R
  • 53 - XGBoost algorithm Run train function using default settings
  • 54 - XGBoost algorithm Hyperparameter optimization model tuning and pairs plot
  • 55 - XGBoost algorithm AUC of ROC plot and important technical error
  • 56 - XGBoost algorithm Run optimized model and probability prediction maps
  • 57 - Levels-key.zip
  • 57 - XGBoost algorithm Classification prediction map production
  • 58 - KNN algorithm Data entry and visualization of target and other variables
  • 58 - LS-KNN.zip
  • 59 - KNN algorithm Run of train function and hyperparameter optimized models
  • 60 - KNN algorithm AUC of ROC plot and technical issues with data entry
  • 61 - KNN algorithm probability prediction maps
  • 62 - KNN algorithm classification prediction map
  • 62 - Levels-key.zip
  • 63 - LS-NB.zip
  • 63 - NB algorithm Training data entry and visualization of variables
  • 64 - NB algorithm Train function and Hyperparameters and AUC of ROC plot
  • 65 - Levels-key.zip
  • 65 - NB algorithm Probability and classification prediction maps production
  • 66 - LS-RF.zip
  • 66 - RF algorithm Data entry of training data variables
  • 67 - RF algorithm default train function and Hyperparameter and AUC of ROC plot
  • 68 - Levels-key.zip
  • 68 - RF algorithm Probability and classification prediction maps
  • 69 - Summary and Visualization of 4 algorithms prediction maps in QGIS

  • 9 - Projects Conclusion and main remarks of the presented course
  • 70 - Summary Let us sum up everything and recap what we discussed earlier
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    شناسه: 1291
    حجم: 12377 مگابایت
    مدت زمان: 950 دقیقه
    تاریخ انتشار: 26 دی 1401
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