Contact

All Stories

  1. Assessing Hyrcanian forest fire vulnerability: socioeconomic and environmental perspectives

    Article • Journal of Forestry Research, February 2025, Springer Science + Business Media

    khalil Gholamnia

  2. Evaluating the Impact of Recursive Feature Elimination on Machine Learning Models for Predicting Forest Fire-Prone Zones

    Article • Fire, November 2024, MDPI AG

    khalil Gholamnia

  3. The application of ResU-net and OBIA for landslide detection from multi-temporal Sentinel-2 images

    Article • Big Earth Data, February 2022, Taylor & Francis

    khalil Gholamnia

  4. A Google Earth Engine Approach for Wildfire Susceptibility Prediction Fusion with Remote Sensing Data of Different Spatial Resolutions

    Article • Remote Sensing, January 2022, MDPI AG

    khalil Gholamnia

  5. Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory

    Article • Journal of Hydrology, November 2020, Elsevier

    khalil Gholamnia

  6. Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria

    Article • Remote Sensing, August 2020, MDPI AG

    khalil Gholamnia

  7. Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping

    Article • Symmetry, April 2020, MDPI AG

    khalil Gholamnia

  8. Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas

    Article • Remote Sensing, November 2019, MDPI AG

    khalil Gholamnia

  9. Forest Fire Susceptibility and Risk Mapping Using Social/Infrastructural Vulnerability and Environmental Variables

    Article • Fire, September 2019, MDPI AG

    khalil Gholamnia

  10. Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection

    Article • Remote Sensing, January 2019, MDPI AG

    khalil Gholamnia

  11. A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping

    Article • Natural Hazards, August 2018, Springer Science + Business Media

    khalil Gholamnia

  12. A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping

    Article • Journal of Spatial Science, August 2018, Taylor & Francis

    omid ghorbanzadeh, Thomas Blaschke, khalil Gholamnia