An assessment model for urban resilience based on the pressure-state-response framework and BP-GA neural network

Jiao, Liudan; Wang, Lvwen; Lu, Hao; Yi, Fan; Zhang, Yu; Wu, Ya

doi:10.1016/j.uclim.2023.101543

Cited by 34 publications

(8 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although this study adopted rigorous data processing and statistical analysis methods to improve the reliability of the research results, it should be noted that the use of a GA-BP neural network for landscape quality assessment is a relatively new method, and its effectiveness in different contexts remains to be further investigated. Future research could compare the results of this method with traditional methods to evaluate its reliability and validity (Xu et al, 2022;Jiao et al,2023).…”

Section: Discussionmentioning

confidence: 99%

“…However, traditional Backpropagation (BP) neural networks have limitations that can be addressed by optimizing the network using a Genetic Algorithm (GA). A mixed training algorithm that combines the strengths of both GA and neural networks can optimize the network and achieve better calculation results (Jiao et al,2023). The proposed comprehensive evaluation method for urban landscape quality aims to provide a scientific basis for urban landscape planning and design, thereby promoting the sustainable development of urban ecology and human society.…”

Section: Introductionmentioning

confidence: 99%

“…UBL is an important component of the urban ecological environment and is of great significance for improving the quality of life and promoting sustainable urban development (Song et al, 2022). Evaluating the quality of UBL is a complex nonlinear problem that requires consideration of multiple influencing factors, such as socio-economic development, ecological environment conditions, and landscape pattern characteristics (Jiao et al,2023;. The GA-BP neural network model can handle large amounts of data and pattern recognition and improve the accuracy and efficiency of evaluation (Hua et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing urban blue-green landscape quality assessment through hybrid Genetic Algorithm-Back Propagation (GA-BP) neural network approach: a case study in Fucheng, China

Fan,

Malik,

et al. 2024

Preprint

View full text Add to dashboard Cite

This study employs an artificial neural network optimization algorithm, enhanced with a Genetic Algorithm-Back Propagation (GA-BP) network, to assess the service quality of urban water bodies and green spaces, aiming to promote healthy urban environments. From an initial set of 95 variables, 29 key variables were selected, including 17 input variables, such as water and green space area, population size, and urbanization rate, six hidden layer neurons, such as patch number, patch density, and average patch size, and one output variable for the comprehensive value of blue-green landscape quality. The results indicate that the GA-BP network achieves an average relative error of 0.94772%, which is superior to the 1.5988% of the traditional BP network. Moreover, it boasts a prediction accuracy of 90% for the comprehensive value of landscape quality from 2015 to 2022, significantly outperforming the BP network's approximate 70% accuracy. This method enhances the accuracy of landscape quality assessment but also aids in identifying crucial factors influencing quality. It provides scientific and objective guidance for future urban landscape structure and layout, contributing to high-quality urban development and the creation of exemplary living areas.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing urban blue-green landscape quality assessment through hybrid Genetic Algorithm-Back Propagation (GA-BP) neural network approach: a case study in Fucheng, China

Fan,

Malik,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In the agricultural system, annual average precipitation, annual average temperature [49], slope, and distance from a water source [50] were selected to characterize the stability of mountainous agricultural production in terms of climate, topography, and water source, and farmland production potential [15] was selected to reflect the production stability of existing farmland. In the urban system, elevation, topographic undulation [16], and the rate of natural population increase [21,51] were selected to describe the stability of urban geography and population change.…”

Section: Sensitivity Indexmentioning

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

View full text Add to dashboard Cite

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.

show abstract

“…Resilience was initially used to describe the mechanical properties of metals. It was later applied to systems ecology [15][16][17] to describe the ability of ecosystems to maintain normal functioning or restore balance after being damaged. The resilience evaluation encompasses both risk and recovery assessments.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Evaluation of Resilience in Abandoned Open-Pit Mine Slopes Based on a Two-Dimensional Cloud Model with Combination Weighting

Jin,

Liu,

Yao

et al. 2024

Mathematics

View full text Add to dashboard Cite

The stability of abandoned open-pit mine slopes and their ecological environment are threatened owing to their fragile, complicated, and uncertain characteristics. This study establishes a novel evaluation indicator system for enhancing mine design and environmental protection insight. The weights in the system are assigned using a combined method, which consists of the game theory, the interval analytic hierarchy process (IAHP), and the entropy weight method (EWM). The IAHP is optimized by the improved radial movement optimal (IRMO) algorithm and the simulated annealing (SA) algorithm to ensure calculation stability and efficiency. Meanwhile, a two-dimensional cloud model (TDCM) is developed to obtain the slope resilience level and visualize the result. This comprehensive evaluation method is applied to three abandoned mine slopes in the Yellow River Basin, and the results demonstrate that the method can provide crucial insights for rational mine slope stabilization and ecological restoration.

show abstract

An assessment model for urban resilience based on the pressure-state-response framework and BP-GA neural network

Cited by 34 publications

References 45 publications

Enhancing urban blue-green landscape quality assessment through hybrid Genetic Algorithm-Back Propagation (GA-BP) neural network approach: a case study in Fucheng, China

Enhancing urban blue-green landscape quality assessment through hybrid Genetic Algorithm-Back Propagation (GA-BP) neural network approach: a case study in Fucheng, China

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

A Comprehensive Evaluation of Resilience in Abandoned Open-Pit Mine Slopes Based on a Two-Dimensional Cloud Model with Combination Weighting

Contact Info

Product

Resources

About