Browsing by Author "Mao, Yimin"

Now showing 1 - 4 of 4

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: 1
Citation - Scopus: 1
Convolutional Neural Network-Based Deep Learning for Landslide Susceptibility Mapping in the Bakhtegan Watershed
(Nature Portfolio, 2025) Feng, Li; Zhang, Maosheng; Mao, Yimin; Liu, Hao; Yang, Chuanbo; Dong, Ying; Nanehkaran, Yaser A.; 01. Çankaya Üniversitesi
Landslides pose a significant threat to infrastructure, ecosystems, and human safety, necessitating accurate and efficient susceptibility assessment methods. Traditional models often struggle to capture the complex spatial dependencies and interactions between geological and environmental factors. To address this gap, this study employs a deep learning approach, utilizing a convolutional neural network (CNN) for high-precision landslide susceptibility mapping in the Bakhtegan watershed, southwestern Iran. A comprehensive landslide inventory was compiled using 235 documented landslide locations, validated through remote sensing and field surveys. An equal number of non-landslide locations were systematically selected to ensure balanced model training. Fifteen key conditioning factors-including topographical, geological, hydrological, and climatological variables-were incorporated into the model. While traditional statistical methods often fail to extract spatial hierarchies, the CNN model effectively processes multi-dimensional geospatial data, learning intricate patterns influencing slope instability. The CNN model outperformed other classification approaches, achieving an accuracy of 95.76% and a precision of 95.11%. Additionally, error metrics confirmed its reliability, with a mean absolute error (MAE) of 0.11864, mean squared error (MSE) of 0.18796, and root mean squared error (RMSE) of 0.18632. The results indicate that the northern and northeastern regions of the Bakhtegan watershed are highly susceptible to landslides, highlighting areas where proactive mitigation strategies are crucial. This study demonstrates that deep learning, particularly CNNs, offers a powerful and scalable solution for landslide susceptibility assessment. The findings provide valuable insights for urban planners, engineers, and policymakers to implement effective risk reduction strategies and enhance resilience in landslide-prone regions.
Citation - WoS: 33
Citation - Scopus: 41
Fuzzy-Based Intelligent Model for Rapid Rock Slope Stability Analysis Using Qslope
(Mdpi, 2023) Mao, Yimin; Chen, Liang; Nanehkaran, Yaser A.; Azarafza, Mohammad; Derakhshani, Reza; 01. Çankaya Üniversitesi
Artificial intelligence (AI) applications have introduced transformative possibilities within geohazard analysis, particularly concerning the assessment of rock slope instabilities. This study delves into the amalgamation of AI and empirical techniques to attain highly precise outcomes in the evaluation of slope stability. Specifically, our primary objective is to propose innovative and efficient methods by investigating the integration of AI within the well-regarded Q(slope) system, renowned for its efficacy in analyzing rock slope stability. Given the complexities inherent in rock characteristics, particularly in coastal regions, the Q(slope) system necessitates adjustments and harmonization with other geomechanical methodologies. Uncertainties prevalent in rock engineering, compounded by water-related factors, warrant meticulous consideration during all calculations. To address these complexities, we present a novel approach through the infusion of fuzzy set theory into the Q(slope) classification, leveraging fuzziness to effectively quantify and accommodate uncertainties. Our approach employs a sophisticated fuzzy algorithm encompassing six inputs, three outputs, and 756 fuzzy rules, thereby enabling a robust assessment of rock slope stability in coastal regions. The implementation of this method capitalizes on the high-level programming language Python, enhancing computational efficiency. To validate the potency of our AI-based approach, we conducted preliminary tests on slope instabilities within coastal zones, indicating a promising initial direction. The results underwent thorough evaluation, affirming the precision and dependability of the proposed method. However, it is crucial to emphasize that this work represents a first attempt to apply AI to the evaluation of rock slope stability. Our findings underscore a high degree of concurrence and expeditious stability assessment, vital for timely and effective hazard mitigation. Nonetheless, we acknowledge that the reliability of this innovative method must be established through broader applications across diverse scenarios. The proposed AI-based approach's effectiveness is validated through a preliminary survey on a slope instability case within a coastal region, and its potential merits must be substantiated through broader validation efforts.
Citation - WoS: 12
Citation - Scopus: 11
Innovative Stability Analysis of Complex Secondary Toppling Failures in Rock Slopes Using the Block Theory
(Springer Heidelberg, 2025) Mao, Yimin; Azarafza, Mohammad; Bonab, Masoud Hajialilue; Pusatli, Tolga; Nanehkaran, Yaser A.; 03.07. Yönetim Bilişim Sistemleri; 03. İktisadi ve İdari Birimler Fakültesi; 01. Çankaya Üniversitesi
We present the block theory-based secondary toppling stability analysis method (BTSTSA), an advanced and novel method specifically designed to assess secondary toppling failures in slopes. This innovative method comprehensively accounts for various failure mechanisms and computes the factor of safety (F.S) for rock slopes. Grounded in Block theory principles, particularly the key-block method, and supplemented by limit equilibrium techniques, BTSTSA offers a practical and reliable analytical framework. Our investigation focused on five discontinuous rock slopes in the South Pars region, southwest Iran, which are affected by composite toppling failure mechanisms. The stability analysis results were meticulously verified using the Aydan-Kawamoto method, a recognized benchmark in the field. Comparative analysis consistently demonstrated that the BTSTSA approach generates more conservative estimates of the F.S compared to the Aydan-Kawamoto method. This conservatism underscores the robustness and reliability of the BTSTSA framework and highlights its implications for practical engineering applications. The integration of this innovative analytical method with data from these investigations offers crucial insights for geotechnical engineers, equipping them to manage the complexities of secondary toppling failures in discontinuous rock slopes. These findings emphasize the importance of considering conservatism in engineering applications and provide a more accurate and reliable assessment of slope stability, particularly concerning secondary toppling failures, thereby benefiting geotechnical engineering practices.