Browsing by Author "Chen, Junde"

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Citation - WoS: 3
Citation - Scopus: 4
Adaptive Modeling of Landslide Susceptibility Using Analytical Hierarchy Process and Multi-Objective Decision Optimization
(Elsevier Sci Ltd, 2025) Mao, Yimin; Zhu, Licai; Chen, Junde; Nanehkaran, Yaser A.; 01. Çankaya Üniversitesi
This study develops a detailed landslide susceptibility map for Kermanshah province, Iran, by analyzing field surveys, historical data, and remote sensing. Fifteen key factors-such as geomorphology, geology, climate, seismicity, and human activities-were identified and ranked using Analytical Hierarchy Process (AHP) and Multi-Objective Decision Optimization (MODO) within a GIS framework. The analysis classifies landslide risk into five categories: very high (18.4%), high (33.98%), moderate (24.19%), low (14.36%), and very low (9.07%). Pixel rate assessment confirmed the map's accuracy, showing that eastern and northeastern regions are particularly prone to landslides, with a substantial portion of the province at moderate to high risk. The study recommends using this map to guide targeted risk mitigation and land-use planning efforts to reduce landslide impacts on vulnerable areas. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Citation - WoS: 73
Citation - Scopus: 82
Application of Machine Learning Techniques for the Estimation of the Safety Factor in Slope Stability Analysis
(Mdpi, 2022) Nanehkaran, Yaser Ahangari; Pusatli, Tolga; Jin Chengyong; Chen, Junde; Cemiloglu, Ahmed; Azarafza, Mohammad; Derakhshani, Reza; 51704; 03.07. Yönetim Bilişim Sistemleri; 03. İktisadi ve İdari Birimler Fakültesi; 01. Çankaya Üniversitesi
Slope stability is the most important stage in the stabilization process for different scale slopes, and it is dictated by the factor of safety (FS). The FS is a relationship between the geotechnical characteristics and the slope behavior under various loading conditions. Thus, the application of an accurate procedure to estimate the FS can lead to a fast and precise decision during the stabilization process. In this regard, using computational models that can be operated accurately is strongly needed. The performance of five different machine learning models to predict the slope safety factors was investigated in this study, which included multilayer perceptron (MLP), support vector machines (SVM), k-nearest neighbors (k-NN), decision tree (DT), and random forest (RF). The main objective of this article is to evaluate and optimize the various machine learning-based predictive models regarding FS calculations, which play a key role in conducting appropriate stabilization methods and stabilizing the slopes. As input to the predictive models, geo-engineering index parameters, such as slope height (H), total slope angle (beta), dry density (gamma(d)), cohesion (c), and internal friction angle (phi), which were estimated for 70 slopes in the South Pars region (southwest of Iran), were considered to predict the FS properly. To prepare the training and testing data sets from the main database, the primary set was randomly divided and applied to all predictive models. The predicted FS results were obtained for testing (30% of the primary data set) and training (70% of the primary data set) for all MLP, SVM, k-NN, DT, and RF models. The models were verified by using a confusion matrix and errors table to conclude the accuracy evaluation indexes (i.e., accuracy, precision, recall, and f1-score), mean squared error (MSE), mean absolute error (MAE), and root mean square error (RMSE). According to the results of this study, the MLP model had the highest evaluation with a precision of 0.938 and an accuracy of 0.90. In addition, the estimated error rate for the MLP model was MAE = 0.103367, MSE = 0.102566, and RMSE = 0.098470.