Master Degree Alumni

Prima Riza Kadavi 


> Kangwon National University Division of Science Education. 2019

      - Application of Machine Learning Technique for Groundwater Potential Mapping

> Jenderal Soedirman University Geological Engineering, (Indonesia). 2016 


>Research topic 

      - Remote sensing data for land cover classification in volcano area 

      - GIS modelling for landslide susceptibility 


> E-mail : rizakadavi@gmail.com 

1) Kadavi, P.-R, Lee, C.-W, S.-R. Lee (2019): Landslide-susceptibility mapping in Gangwon-do, South Korea, using logistic regression and decision tree models, Environmental Earth Sciences, Vol. 78, NO. 116.

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2) Syifa, M, Kadavi, P.-R, Lee, C.-W (2019): An Artificial Intelligence Application for Post-Earthquake Damage Mapping in Palu, Central Sulawesi, Indonesia, Sensors, Vol. 19, NO. 3, 542.

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3) Kadirhodjaev, A.-Z., Kadavi, P.-R, Lee, C.-W & S.-R. Lee (2018): Analysis of the relationships between topographic factors and landslide occurrence and their application to landslide susceptibility mapping: a case study of Mingchukur, Uzbekistan, Geosciences Journal, Vol. 22, NO. 6, 1053–1067.

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4) Oh, H.-J., Kadavi, P.-R, Lee, C.-W & S.-R. Lee (2018): Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models, Geomatics, Natural Hazards and Risk, Vol. 9, NO. 1, 1053–1070.

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5) Park, S.-J., Kadavi, P.-R, C.-W. Lee*(2018): Landslide Susceptibility Apping and Comparison Using Probabilistic Models: A Case Study of Sacheon, Jumunzin Area, Korea , Korea Journal of Remote Sensing, Vol. 34, No. 5, 721-738.

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6) Prima Riza, K. and Lee, C.-W and S.-R. Lee (2018): Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping, Remote Sensing, 10(8), 1252.

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7) Prima Riza, K. and Lee, C.-W( 2018): Land cover classification analysis of volcanic island in Aleutian Arc using an Artificial Neural Network (ANN) and a Support Vector Machine (SVM) from Landsat imagery, Geosciences Journal, Vol. 22, Issue 4, pp 653–665.

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8) Prima Riza, K., W.-J, Lee and Lee, C.-W (2017): Analysis of the Pyroclastic Flow Deposits of Mount Sinabung and Merapi Using Landsat Imagery and the Artificial Neural Networks Approach. Applied Sciences, Vol.7, No.9, 935-949.

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Kangwon National University Earth Observation Laboratory 

 KNU Chuncheon Campus 1, Gangwondaehakgil, Chuncheon-si, 24341 Republic of Korea 

 Kangwon National University College of Education #4 - 301. TEL : +82.33.250.7923