A spatial evaluation method for earthquake disaster using optimized BP neural network model

Zhou, Hui; Che, Ailan; Xiang-hua, Shuai; Zhang, Yi

doi:10.1080/19475705.2022.2160664

Cited by 9 publications

(5 citation statements)

References 48 publications

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“…By constructing a four-layer BP neural network, we obtained the optimal relationship between each indicator and the vulnerability results. Accuracy verification revealed that, compared to the standard BP neural network commonly used in the vulnerability field [66,67], the GA-BP neural network with double hidden layers had superior generalization capability, avoided local optima, and greatly improved the assessment accuracy. This is because the genetic algorithm determined the optimal weights and thresholds by searching for individuals with higher fitness, significantly reducing the error of the results [68,69].…”

Section: Applicability Of the Assessment Frameworkmentioning

confidence: 99%

Spatial Vulnerability Assessment for Mountain Cities Based on the GA-BP Neural Network: A Case Study in Linzhou, Henan, China

Duan,

Yu,

Sun

et al. 2024

Land

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Mountain cities with complex topographies have always been highly vulnerable areas to global environmental change, prone to geological hazards, climate change, and human activities. Exploring and analyzing the vulnerability of coupling systems in mountain cities is highly important for improving regional resilience and promoting sustainable regional development. Therefore, a comprehensive framework for assessing the spatial vulnerability of mountain cities is proposed. A vulnerability assessment index system is constructed using three functional systems, ecological protection, agricultural production, and urban construction. Subsequently, the BP neural network and the genetic algorithm (GA) are combined to establish a vulnerability assessment model, and geographically weighted regression (GWR) is introduced to analyze the spatial influence of one-dimensional systems on the coupling system. Linzhou, a typical mountain city at the boundary between China’s second- and third-step terrains, was selected as a case study to demonstrate the feasibility of the framework. The results showed that the vulnerability of the ecological protection system was highly aggregated in the east–central region, that of the agricultural production system was high in the west, and that of the urban construction system was low in the central region and high in the northwestern region. The coupling system vulnerability was characterized by multispatial distribution. The complex topography and geomorphology and the resulting natural hazards are the underlying causes of the vulnerability results. The impact of ecological and urban systems on the coupling system vulnerability is more prominent. The proposed framework can serve as a reference for vulnerability assessments of other similar mountain cities with stepped topographies to support the formulation of sustainable development strategies.

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Section: Applicability Of the Assessment Frameworkmentioning

confidence: 99%

Spatial Vulnerability Assessment for Mountain Cities Based on the GA-BP Neural Network: A Case Study in Linzhou, Henan, China

Duan,

Yu,

Sun

et al. 2024

Land

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“…However, the KNN method has significant computational complexity and may perform poorly in cases of imbalanced sample distribution. The BP neural network is a multi-layer feedforward neural network that automatically adjusts model parameters for network training through error backpropagation algorithm which has strong nonlinear mapping ability and flexible network structure Zhou H, et al [5,6] allowing for arbitrary adjustment of the number of intermediate layers and the number of neurons in each layer according to specific situations. However, it also has some drawbacks: on the one hand, it is prone to falling into local minima; on the other hand, its learning speed is slow, and there are certain limitations in its generalization ability.…”

Section: Introductionmentioning

confidence: 99%

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2025

OAJA

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Redshift is an important parameter of galaxies, and the use of photometry data for redshift estimation has always been a focus in the field of astronomy. This study explores a redshift estimation method for SDSS photometric data based on the AutoGluon-Mix algorithm, in which the integrated learning methods, including K-nearest neighbors, random forests, XGBoosted trees, LightGBM boosted trees, CatBoost boosted trees, Extremely Randomized Trees, and neural networks, improve the accuracy of redshift estimation through comprehensive strategies. The experimental results show that AutoGluon-Mix performs extremely well compared to other algorithms for both early and late type galaxies.

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“…In nonlinear fitting, back propagation neural network (BPNN), which has strong nonlinear mapping ability, is widely used in modeling. [12][13][14] In terms of intelligent optimization algorithms, slime mold algorithm (SMA) simulates the behavior strategies of slime molds when searching for food. [15][16][17] It has the characteristics of a simple structure, fast optimization speed, and can achieve a balance between local optimization and global search.…”

Section: Introductionmentioning

confidence: 99%

“…In nonlinear fitting, back propagation neural network (BPNN), which has strong nonlinear mapping ability, is widely used in modeling 12–14 . In terms of intelligent optimization algorithms, slime mold algorithm (SMA) simulates the behavior strategies of slime molds when searching for food 15–17 .…”

Section: Introductionmentioning

confidence: 99%

Optimization of geometric parameters of hydraulic turbine runner in turbine mode based on ISMA and BPNN

Wang,

Chen,

Zhang

et al. 2023

Energy Science & Engineering

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The hydraulic turbine in turbine mode (TMHT) has significant advantages in residual energy recovery, but rapid optimization of its runner parameters has always been a challenge. To address this issue, the optimization algorithm ISMA‐BPNN is proposed based on the improved slime mold algorithm (ISMA) and back propagation neural network (BPNN). First, an efficiency characteristic neural network (ECNN) and a water head characteristic neural network (HCNN), which take the geometric parameters of the runner as input, and the efficiency and water head as outputs, respectively, are constructed by combining the orthogonal test‐based sample data, computational fluid dynamics (CFD), and BPNN. Then, the slime mold algorithm is improved and the optimal runner geometric parameters are obtained based on the ISMA, ECNN, and HCNN, to achieve rapid optimization of the TMHT runner. Finally, the CFD‐based numerical calculation accuracy is verified through real machine tests, and the feasibility of the ISMA‐BPNN‐based rapid optimization strategy for TMHT performance is further verified through CFD numerical simulation.

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A spatial evaluation method for earthquake disaster using optimized BP neural network model

Cited by 9 publications

References 48 publications

Spatial Vulnerability Assessment for Mountain Cities Based on the GA-BP Neural Network: A Case Study in Linzhou, Henan, China

Spatial Vulnerability Assessment for Mountain Cities Based on the GA-BP Neural Network: A Case Study in Linzhou, Henan, China

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Optimization of geometric parameters of hydraulic turbine runner in turbine mode based on ISMA and BPNN

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