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- 저자 : Zhehao Lin (검색결과 2 건)
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Hao Cui,He-Chun Quan,Ri Jin,Zhehao Lin 대한토목학회 2023 KSCE JOURNAL OF CIVIL ENGINEERING Vol.27 No.1
Flood susceptibility mapping is an important method for flood research. In this paper, we combine a backpropagation neural network (BPNN) with a genetic quantum algorithm (GQA) for the first time to develop flood susceptibility mapping. The area on the Chinese side of the Tumen River Basin was selected as the research object. A set of flood conditioning factors was selected based on relevant literature and an actual situation and then validated using the chi-square test and correlation analysis. Different weights were assigned using stepwise weight assessment ratio analysis. Finally, modeling and flood susceptibility mapping using GQA-BPNN. As a reference, the same work was performed with both the pure BPNN and optimized BPNN using a genetic algorithm (GA). The results show that the area under the curve, root mean squared error, Nash-Sutcliffe coefficient and percentage of bias are significantly better for the GQA-BPNN than for the BPNN and GA-BPNN and that the flood sensitivity maps constructed by the GQA-BPNN have more flood points in high flood sensitivity areas. Therefore, the GQA-BPNN method can be considered an effective method for flood susceptibility mapping.
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Assessment of Landslide Susceptibility using the PCA and ANFIS with Various Metaheuristic Algorithms
Zelu Chen,Hechun Quan,Ri Jin,Aifen Jin,Zhehao Lin,Guangri Jin,Guang-Zhu Jin 대한토목학회 2024 KSCE Journal of Civil Engineering Vol.28 No.4
It is very important for the susceptibility assessment and disaster prediction of the region to effectively evaluate the landslide susceptibility. In this study, Particle Swarm Optimization (PSO), Artificial Bee Colony algorithm (ABC), Shuffled Frog Leaping Algorithm (SFLA) and Bat algorithm (BAT) are used to optimize Adaptive Neuro-Fuzzy Inference System (ANFIS) to evaluate the landslide susceptibility. 811 sample points were collected through remote sensing analysis and field investigation for susceptibility analysis. Fifteen landslide evaluation factors were quantified and normalized, and the Principal Component Analysis (PCA) method was used to compress them into 6 main factors. The accuracy analysis results of the area under the curve (AUC), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) evaluation models show that the AUC values of PSO, ABC, SFLA and BAT are 93.6%, 96.2%, 90.8% and 86.1%, respectively. Among them, the accuracy of ABC is the highest. This study effectively evaluates the landslide susceptibility through a new neural network hybrid method, which provides a theoretical basis for landslide disaster susceptibility management.
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