Akademisyen profili · DOÇENT
EMREHAN KUTLUĞ ŞAHİN
BOLU ABANT İZZET BAYSAL ÜNİVERSİTESİ
- Ana Dal Mühendislik Temel Alanı
- Yan Dal Harita Mühendisliği
- MÜHENDİSLİK FAKÜLTESİ
- İNŞAAT MÜHENDİSLİĞİ BÖLÜMÜ
Scopus (SJR)
Q1
17
Q2
7
Q3
0
Q4
0
WoS (JCR)
Q1
11
Q2
12
Q3
2
Q4
0
TR Index
6
makale
Makaleler
- 2025 FastLSM-AutoML: Fast, reliable, and robust end-to-end AutoML tool for producing a landslide susceptibility map
- 2025 Geoscience in the era of generative artificial intelligence (Geo[AI]-LSM): understanding the potential benefits of Google Gemini in producing landslide susceptibility mapping
- 2025 CatGrass: a feature engineering framework to forecast the seismic response of low-rise RC frames using CatBoost algorithm integrated with grasshopper optimization
- 2025 An innovative machine learning approach for slope stability prediction by combining shap interpretability and stacking ensemble learning
- 2025 Machine Learning Based Prediction of Peak Floor Acceleration in Low- to Mid-Rise RC Buildings Using Ground Motion Intensity Measures
- 2024 The effectiveness of data pre-processing methods on the performance of machine learning techniques using RF, SVR, Cubist and SGB: a study on undrained shear strength prediction
- 2024 Advanced tree-based machine learning methods for predicting the seismic response of regular and irregular RC frames
- 2023 Greedy-AutoML: A Novel Greedy-Based Stacking Ensemble Learning Framework for Assessing Soil Liquefaction Potential
- 2023 Assessing the predictive capability of DeepBoost machine learning algorithm powered by hyperparameter tuning methods for slope stability prediction
- 2023 Application of state-of-the-art machine learning algorithms for slope stability prediction by handling outliers of the dataset
- 2023 Predicting occurrence of liquefaction-induced lateral spreading using gradient boosting algorithms integrated with particle swarm optimization: PSO-XGBoost, PSO-LightGBM, and PSO-CatBoost
- 2023 Implementation of free and open-source semi-automatic feature engineering tool in landslide susceptibility mapping using the machine-learning algorithms RF, SVM, and XGBoost
- 2023 Random Forest Importance-Based Feature Ranking and Subset Selection for Slope Stability Assessment using the Ranger Implementation
- 2022 Liquefaction prediction with robust machine learning algorithms (SVM, RF, and XGBoost) supported by genetic algorithm-based feature selection and parameter optimization from the perspective of data processing
- 2022 Comparison of tree-based machine learning algorithms for predicting liquefaction potential using canonical correlation forest, rotation forest, and random forest based on CPT data
- 2022 An investigation of feature selection methods for soil liquefaction prediction based on tree-based ensemble algorithms using AdaBoost, gradient boosting, and XGBoost
- 2022 Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping
- 2021 Performance analysis of advanced decision tree-based ensemble learning algorithms for landslide susceptibility mapping
- 2021 Assessment of Feature Selection for Liquefaction Prediction Based on Recursive Feature Elimination
- 2020 Developing comprehensive geocomputation tools for landslide susceptibility mapping: LSM tool pack