Browsing by Author "Feng, Li"

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Citation - WoS: 21
Citation - Scopus: 27
Compound usage of L shaped fin and Nano-particles for the acceleration of the solidification process inside a vertical enclosure (A comparison with ordinary double rectangular fin)
(Elsevier, 2021) Chen, Yuning; Jarad, Fahd; Feng, Li; Jamal, Sajjad Shaukat; Sharma, Kamal; Mahariq, Ibrahim; Jarad, Fahd; Arsalanloo, Akbar; 234808; Matematik
Performance enhancement of energy storing unite is a very important issue in renewable energy systems. In the present investigation, L shaped fins with two different orientations and various geometrical properties were considered to enhance the solidification performance inside a vertical enclosure. Furthermore, effect of two types of Nano-particles with different concentrations were evaluated. It was found that the application of downward L shaped fin is better than upward form in solidification process. Also, results revealed that case with same length in vertical and horizontal part of L shaped fin has best performance. Furthermore, results presented that there is no need to use high cost Nano-particles of Cu because low cost Nano-particles of Al2O3 have better performance in considered issue. Best performance was related to case 3 which had 12.3% enhancement in the solidification time. Also, between the downward fins, the best case was related to case 8 which provided about 21.5% reduction in the solidification time. Furthermore, it was found that the application of AL(2)O(3) with concertation of 2% and case 8, provided about 27% improvement in the total melting time when compared to the Base case.
Citation - WoS: 0
Citation - Scopus: 0
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.
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.