Estimation of soil liquefaction using artificial intelligence techniques: an extended comparison between machine and deep learning approaches
Şehmusoğlu, Eyyüp Hakan | Kurnaz, Talas Fikret | Erden, Caner
This study investigates the effectiveness of various deep learning (DL) algorithms in predicting soil liquefaction susceptibility. We explore a spectrum of algorithms, including machine learning models such as Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), and Logistic Regression (LR), alongside DL architectures like Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Bidirectional LSTMs (BiLSTMs), and Gated Recurrent Units (GRUs). The performance of these algorithms is assessed using comprehensive metrics, including accuracy, precision, recall, F1-score, receiver operating characteristic (ROC) curve analysis, and area under the curve (AUC). Cross-entropy loss is employed as the loss function during model training to optimize the differentiation...
Monitoring landslides with geophysical and geodetic observations
Şanlıoğlu, İsmail
The objective of this study was to evaluate and predict land movement by integrating geodetic, geophysical and meteorological data in a landslide area. Specifically, electrical resistivity tomography surveying, Global Navigation Satellite System and terrestrial laser scanning techniques were integrated to monitor a landslide. The study area lies to the southeast of the town of Ta kent in southern Turkey, close to Balcflar in the Central Taurus mountain chain. Landslides result in considerable damage to structures, farmland and the environment in this area; therefore, it is important to characterise the size, extent and timing of past land movements in order to mitigate damage from future landslides. Analysis presented in this paper shows that the greatest land movements in the region occur...
Explainable AI using ensemble machine learning with integrated SHapley additive explanations (SHAP)-Borda approach for estimation of the safety factor against soil liquefaction
Dağdeviren, Uğur | Demir, Alparslan Serhat | Erden, Caner | Kökçam, Abdullah Hulusi | Kurnaz, Talas Fikret
In most of the studies on soil liquefaction prediction based on Machine Learning (ML), the models presented are presented in a closed box structure. In the studies where the effect of the features on the model performance is analyzed with Interpretability methods, it is seen that the order of effect of the features changes for each ML algorithm. This situation makes the results of the studies conducted on the same subject inconsistent. In this study, we propose an integrated SHapley Additive exPlanations (SHAP)-Borda approach to overcome this problem. With this study, we provide decision makers with ease in explaining ML models by combining SHAP analysis results with the Borda method for the first time. In the study, ensemble ML algorithms were used for soil liquefaction prediction u...