A Graph Neural Network (GNN) Algorithm for Constructing the Evolution Process of Rural Settlement Morphology

Hu, Zhe; Chen, Kexin; Xie, Xiaofei

doi:10.1155/2022/7517347

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…The PSO-BP algorithm is a hybrid algorithm that combines particle swarm optimization (PSO) and backpropagation (BP) algorithms. Its principle is to combine the PSO algorithm and BP algorithm, using the particle swarm optimization algorithm to optimize the initial weight and threshold of the neural network, and then train the neural network through the backpropagation algorithm [6] . The particle swarm optimization algorithm is an optimization algorithm based on swarm intelligence, which simulates the behavior of birds foraging for food by constantly adjusting the position and speed of particles to find the optimal solution.…”

Section: Pso-bp Algorithmmentioning

confidence: 99%

Surface settlement monitoring in green mining areas based on PSO-BP neural network algorithm

Zhang,

Wang

2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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A neural network algorithm based on Small Baseline Subsets Interferometric Synthetic Aperture Radar (SBAS-InSAR) and Particle Swarm Optimization-Back Propagation (PSO-BP) is proposed. Aperture Radar (SBAS-InSAR) and Particle Swarm Optimization-Back Propagation (PSO-BP) neural network algorithms are proposed as a model for surface settlement monitoring in mining areas. Firstly, the SBAS-InSAR technology is used to obtain the monitoring values of mine surface settlement; then, the PSO-BP prediction model is constructed from a multi-factor perspective by selecting the influencing factors of mine surface settlement and the obtained settlement monitoring values; finally, the validity and reasonableness of this method are analyzed. The experimental results show that SBAS-InSAR can effectively monitor the long-time subsidence of the mine surface, and with the increase of training samples, the residual difference between the PSO-BP prediction value and the SBAS-InSAR subsidence value gradually decreases, and the algorithm convergence iteration speeds up, and the mean-square error decreases. Comparison with the existing monitoring methods and prediction models proves the advantages of SBAS-InSAR in the monitoring of long-time surface subsidence in mining areas.

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Section: Pso-bp Algorithmmentioning

confidence: 99%

Surface settlement monitoring in green mining areas based on PSO-BP neural network algorithm

Zhang,

Wang

2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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“…Furthermore, the authors in [33] present a Recurrent Neural Network (RNN) and satellite image-based real-time traffic-speed prediction algorithm. To increase forecast accuracy, the model integrates satellite pictures and the RNN to collect temporal trends in traffic data.…”

Section: Graph Neural Network-based Approachesmentioning

confidence: 99%

A Graph Neural Network (GNN)-Based Approach for Real-Time Estimation of Traffic Speed in Sustainable Smart Cities

Sharma

Nikashina

et al. 2023

Sustainability

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Planning effective routes and monitoring vehicle traffic are essential for creating sustainable smart cities. Accurate speed prediction is a key component of these efforts, as it aids in alleviating traffic congestion. While their physical proximity is important, the interconnection of these road segments is what significantly contributes to the increase of traffic congestion. This interconnectedness poses a significant challenge to increasing prediction accuracy. To address this, we propose a novel approach based on Deep Graph Neural Networks (DGNNs), which represent the connectedness of road sections as a graph using Graph Neural Networks (GNNs). In this study, we implement the proposed approach, called STGGAN, for real-time traffic-speed estimation using two different actual traffic datasets: PeMSD4 and PeMSD8. The experimental results validate the prediction accuracy values of 96.67% and 98.75% for the PeMSD4 and PeMSD8 datasets, respectively. The computation of mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) also shows a progressive decline in these error values with increasing iteration count, demonstrating the success of the suggested technique. To confirm the feasibility, reliability, and applicability of the suggested STGGAN technique, we also perform a comparison analysis, including several statistical, analytical, and machine-learning- and deep-learning-based approaches. Our work contributes significantly to the field of traffic-speed estimation by considering the structure and characteristics of road networks through the implementation of DGNNs. The proposed technique trains a neural network to accurately predict traffic flow using data from the entire road network. Additionally, we extend DGNNs by incorporating Gated Graph Attention Network (GGAN) blocks, enabling the modification of the input and output to sequential graphs. The prediction accuracy of the proposed model based on DGNNs is thoroughly evaluated through extensive tests on real-world datasets, providing a comprehensive comparison with existing state-of-the-art models for traffic-flow forecasting.

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“…The primary methods are morphological index analysis [37,38], spatial statistics [39,40], and influencing factor detection [41,42]. Models such as cellular automata or system dynamics combined with intelligent algorithms have been utilized to describe the historical spatial rules of rural settlements [43][44][45][46][47][48]. However, these methods are more suitable for quantitative analysis of the spatial morphological characteristics of settlements at a macro-regional scale, and it is challenging to build micro-level connections between spatial functions and systems.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Spatial Functions on the Public Space System of Traditional Settlements

2023

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Public spaces are important places for various social functions and are also essential components of the cultural heritage of traditional settlements, which are suffering from blind function renewal and structural reconstruction during urbanization. It is essential to clarify the influence of spatial functions on spatial systems for the sustainable development of traditional settlements. Thus, based on the topology theory, we built a research framework for the relationship between public space functions and systems. Through field research, Pajek topological network analysis, and space syntax, we investigated the influence of spatial functions on the public space system of traditional settlements in terms of both structure and order. The research results of Zhang Guying Village demonstrate that (1) the public space of Zhang Guying Village has the structural characteristics of “axis-oriented and multi-core”; (2) the coordinated development of political, living, and traffic public spaces constitutes the formation logic of the public space system; (3) topological analysis of public spaces based on spatial functions helps in the optimization and functional reorganization of the public space system in traditional settlements. Hence, this study can provide understanding of the formation of the public space system and can also provide local administration adaptive reconfiguration strategies for the sustainable development of traditional settlements in the future.

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A Graph Neural Network (GNN) Algorithm for Constructing the Evolution Process of Rural Settlement Morphology

Cited by 6 publications

References 20 publications

Surface settlement monitoring in green mining areas based on PSO-BP neural network algorithm

Surface settlement monitoring in green mining areas based on PSO-BP neural network algorithm

A Graph Neural Network (GNN)-Based Approach for Real-Time Estimation of Traffic Speed in Sustainable Smart Cities

The Influence of Spatial Functions on the Public Space System of Traditional Settlements

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