| Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression T Kavzoglu, EK Sahin, I Colkesen Landslides 11, 425-439, 2014 | 624 | 2014 |
| Susceptibility mapping of shallow landslides using kernel-based Gaussian process, support vector machines and logistic regression I Colkesen, EK Sahin, T Kavzoglu Journal of African Earth Sciences 118, 53-64, 2016 | 188 | 2016 |
| Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest EK Sahin SN Applied Sciences 2 (7), 1308, 2020 | 186 | 2020 |
| Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm T Kavzoglu, EK Sahin, I Colkesen Engineering Geology 192, 101-112, 2015 | 183 | 2015 |
| An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district T Kavzoglu, E Kutlug Sahin, I Colkesen Natural Hazards 76, 471-496, 2015 | 151 | 2015 |
| Machine learning techniques in landslide susceptibility mapping: a survey and a case study T Kavzoglu, I Colkesen, EK Sahin Landslides: Theory, practice and modelling, 283-301, 2019 | 147 | 2019 |
| A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping EK Sahin, I Colkesen, T Kavzoglu Geocarto International 35 (4), 341-363, 2020 | 112 | 2020 |
| Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping EK Sahin Geocarto International 37 (9), 2441-2465, 2022 | 79 | 2022 |
| Performance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mapping E Kutlug Sahin, I Colkesen Geocarto International 36 (11), 1253-1275, 2021 | 67 | 2021 |
| Developing comprehensive geocomputation tools for landslide susceptibility mapping: LSM tool pack EK Sahin, I Colkesen, SS Acmali, A Akgun, AC Aydinoglu Computers & geosciences 144, 104592, 2020 | 58 | 2020 |
| An investigation of feature selection methods for soil liquefaction prediction based on tree-based ensemble algorithms using AdaBoost, gradient boosting, and XGBoost S Demir, EK Sahin Neural Computing and Applications 35 (4), 3173-3190, 2023 | 46 | 2023 |
| Comparison of tree-based machine learning algorithms for predicting liquefaction potential using canonical correlation forest, rotation forest, and random forest based on CPT data S Demir, EK Sahin Soil Dynamics and Earthquake Engineering 154, 107130, 2022 | 42 | 2022 |
| Bulut Bilişim Teknolojisi Ve Bulut Cbs Uygulamalari T Kavzoğlu, EK Şahin IV. Uzaktan Algılama ve Coğrafi Bilgi Sistemleri Sempozyumu (UZAL-CBS 2012 …, 2012 | 42 | 2012 |
| Investigation of automatic feature weighting methods (Fisher, Chi-square and Relief-F) for landslide susceptibility mapping E Kutlug Sahin, C Ipbuker, T Kavzoglu Geocarto International 32 (9), 956-977, 2016 | 36 | 2016 |
| Predicting occurrence of liquefaction-induced lateral spreading using gradient boosting algorithms integrated with particle swarm optimization: PSO-XGBoost, PSO-LightGBM, and … S Demir, EK Sahin Acta Geotechnica 18 (6), 3403-3419, 2023 | 28 | 2023 |
| Liquefaction prediction with robust machine learning algorithms (SVM, RF, and XGBoost) supported by genetic algorithm-based feature selection and parameter optimization from … S Demir, EK Şahin Environmental Earth Sciences 81 (18), 459, 2022 | 28 | 2022 |
| Heyelan duyarlılığının incelenmesinde regresyon ağaçlarının kullanımı: Trabzon örneği T Kavzoğlu, EK Şahin, İ Çölkesen Harita Dergisi 147 (3), 21-33, 2012 | 26 | 2012 |
| Implementation of free and open-source semi-automatic feature engineering tool in landslide susceptibility mapping using the machine-learning algorithms RF, SVM, and XGBoost EK Sahin Stochastic Environmental Research and Risk Assessment 37, 1067–1092, 2023 | 17 | 2023 |
| Evaluation of oversampling methods (OVER, SMOTE, and ROSE) in classifying soil liquefaction dataset based on SVM, RF, and Naïve Bayes S Demir, EK Şahin Avrupa Bilim ve Teknoloji Dergisi, 142-147, 2022 | 17 | 2022 |
| Greedy-AutoML: A novel greedy-based stacking ensemble learning framework for assessing soil liquefaction potential EK Sahin, S Demir Engineering Applications of Artificial Intelligence 119, 105732, 2023 | 16 | 2023 |
Ismail ColkesenAssoc Professor of Geomatics Engineering, Gebze Technical UniversityVerified email at gtu.edu.tr
