P
Paraskevas Tsangaratos
Researcher at National Technical University of Athens
Publications - 56
Citations - 2514
Paraskevas Tsangaratos is an academic researcher from National Technical University of Athens. The author has contributed to research in topics: Landslide & Topographic Wetness Index. The author has an hindex of 18, co-authored 47 publications receiving 1628 citations. Previous affiliations of Paraskevas Tsangaratos include Ton Duc Thang University & China Earthquake Administration.
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Journal ArticleDOI
Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size
TL;DR: Overall, landslide susceptibility assessments could serve as a useful tool for the local and national authorities, in order to evaluate strategies to prevent and mitigate the adverse impacts of landslide events.
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Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China.
TL;DR: A novel approach to construct a flood susceptibility map in the Poyang County, JiangXi Province, China is proposed by implementing fuzzy weight of evidence (fuzzy-WofE) and data mining methods and the fuzzy WofE-SVM model was the model with the highest predictive performance.
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Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment
TL;DR: The proposed DLNN model had a higher performance than the four benchmark models and highlights that the usage of deep learning approach could be considered as a satisfactory alternative approach for landslide susceptibility mapping.
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Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility
Wei Chen,Mahdi Panahi,Paraskevas Tsangaratos,Himan Shahabi,Ioanna Ilia,Somayeh Panahi,Shaojun Li,Abolfazl Jaafari,Baharin Bin Ahmad +8 more
TL;DR: The proposed novel approach, which combines expert knowledge, neuro-fuzzy inference systems and evolutionary algorithms, can be applied for land use planning and spatial modeling of landslide susceptibility.
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Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods.
TL;DR: It can be concluded that the usage of different statistical metrics, provides different outcomes concerning the best prediction model, which mainly could be attributed to sites specific settings.