Paraskeuas Tsaggaratos

Short Biography

Dr. Paraskevas Tsangaratos holds a Diploma in Mining and Metallurgical Engineering and a Ph.D. in Engineering from the National Technical University of Athens (NTUA). He is an Associate Professor at the School of Mining and Metallurgical Engineering of NTUA, in the Section of Geological Sciences and the Laboratory of Engineering Geology and Hydrogeology, with the academic field “Geoinformatics in Geosciences with Emphasis on Geohazards.” His doctoral dissertation focused on the investigation of the engineering geological behavior of geological formations using information systems.

His teaching activities cover a broad range of subjects linking geosciences with modern computational and geospatial technologies. He teaches, among others, the courses “Introduction to Programming (Python),” “Artificial Intelligence and Machine Learning,” “Geographic Information Systems,” and “Natural Disaster Management,” as well as laboratory exercises in “Engineering Geology and Geohazards” and “Engineering Geology of Mining and Civil Engineering Projects.” At postgraduate level, he also teaches the course “Applications of Geoinformatics in Mining” within the NTUA Interdepartmental Postgraduate Programme in Geoinformatics. In parallel, he supervises doctoral, postdoctoral, postgraduate, and diploma theses in contemporary fields of geosciences.

Selected Publications

• Tsangaratos, P., Vakalas, I., Zanarini, I., 2025. Distinguishing Lithofacies of Flysch Formations Using Deep Learning Models: Integrating Remote Sensing Data with Morphological Indexes. *Remote Sensing*, 17(3), 422. https://doi.org/10.3390/rs17030422
  This study represents a recent direction of his research toward deep learning, remote sensing, and automated geological mapping.


• Tsangaratos, P., Ilia, I., Chrysafi, A.-A., Matiatos, I., Chen, W., Hong, H., 2023. Applying a 1D Convolutional Neural Network in Flood Susceptibility Assessments—The Case of the Island of Euboea, Greece. *Remote Sensing*, 15(14), 3471. https://doi.org/10.3390/rs15143471
  This work combines deep learning, spatial analysis, and flood susceptibility assessment in a Greek case study.


• Bui, D.T., Tsangaratos, P., Nguyen, V.-T., Liem, N.V., Trinh, P.T., 2020. Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment. *CATENA*, 188, 104426.
  A representative study of the transition from conventional machine learning methods to deep learning for landslide susceptibility prediction.


• Chen, W., Zhao, X., Tsangaratos, P., Shahabi, H., Ilia, I., Xue, W., Wang, X., Ahmad, B.B., 2020. Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping. *Journal of Hydrology*, 583, 124602.
  This study broadens his research scope toward hydrogeology and the application of ensemble machine-learning techniques.


• Bui, D.T., Tsangaratos, P., Ngo, P.-T.T., Pham, T.D., Pham, B.T., 2019. Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods. *Science of the Total Environment*, 668, 1038–1054.
  This work highlights the application of advanced computational methods to flash-flood susceptibility assessment.


• Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A-X., Xu, C., 2018. Applying Genetic Algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China. *Science of the Total Environment*, 630, 1044–1056.
  This study demonstrates the expansion of spatial analysis and data-mining applications beyond landslides to forest-fire susceptibility.


• Tsangaratos, P., Kallioras, A., Pizpikis, T., Vasileiou, E., Ilia, I., Pliakas, F., 2017. Multi-criteria Decision Support System (DSS) for optimal locations of Soil Aquifer Treatment (SAT) facilities. *Science of the Total Environment*, 603, 472–486.
  A representative application of GIS, multicriteria analysis, and decision-support systems to hydrogeological and environmental management problems.


• Tsangaratos, P., Ilia, I., Hong, H., Chen, W., Xu, C., 2017. Applying Information Theory and GIS-based quantitative methods to produce landslide susceptibility maps in Nancheng County, China. *Landslides*, 14(3), 1091–1111.
  A key publication in his work on landslide susceptibility modeling using quantitative and GIS-based methods.


• Tsangaratos, P., Ilia, I., 2016. Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size. *CATENA*, 145, 164–179.
  One of his most representative studies comparing machine-learning algorithms for geohazard assessment.


• Svigkas, N., Papoutsis, I., Loupasakis, C., Tsangaratos, P., Kiratzi, A., Kontoes, C., 2016. Land subsidence rebound detected via multi-temporal InSAR and ground truth data in Kalochori and Sindos regions, Northern Greece. *Engineering Geology*, 209, 175–186.
  This publication reflects another major research direction, namely ground deformation, engineering geology, and the use of satellite InSAR techniques.