Improving an Urban Cellular Automata Model Based on Auto-Calibrated and Trend-Adjusted Neighborhood

Pan, Xinhao; Wang, Zichen; Miao-fen, Huang; Liu, Zhifeng

doi:10.3390/land10070688

Cited by 9 publications

(3 citation statements)

References 40 publications

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“…This mechanism allows CA models to exhibit local features in spatial simulations. However, it also imposes local constraints on CA models during land simulation [38]. Specifically, when a certain land use type is absent in a region, the neighborhood effect for that land type among all cells in the region is 0.…”

Section: Introductionmentioning

confidence: 99%

VST-PCA: A Land Use Change Simulation Model Based on Spatiotemporal Feature Extraction and Pre-Allocation Strategy

Liu,

Luo,

Wang

et al. 2024

IJGI

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Land use/cover change (LUCC) refers to the phenomenon of changes in the Earth’s surface over time. Accurate prediction of LUCC is crucial for guiding policy formulation and resource management, contributing to the sustainable use of land, and maintaining the health of the Earth’s ecosystems. LUCC is a dynamic geographical process involving complex spatiotemporal dependencies. Existing LUCC simulation models suffer from insufficient spatiotemporal feature learning, and traditional cellular automaton (CA) models exhibit limitations in neighborhood effects. This study proposes a cellular automaton model based on spatiotemporal feature learning and hotspot area pre-allocation (VST-PCA). The model utilizes the video swin transformer to acquire transformation rules, enabling a more accurate capture of the spatiotemporal dependencies inherent in LUCC. Simultaneously, a pre-allocation strategy is introduced in the CA simulation to address the local constraints of neighborhood effects, thereby enhancing the simulation accuracy. Using the Chongqing metropolitan area as the study area, two traditional CA models and two deep learning-based CA models were constructed to validate the performance of the VST-PCA model. Results indicated that the proposed VST-PCA model achieved Kappa and FOM values of 0.8654 and 0.4534, respectively. Compared to other models, Kappa increased by 0.0322–0.1036, and FOM increased by 0.0513–0.1649. This study provides an accurate and effective method for LUCC simulation, offering valuable insights for future research and land management planning.

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Section: Introductionmentioning

confidence: 99%

VST-PCA: A Land Use Change Simulation Model Based on Spatiotemporal Feature Extraction and Pre-Allocation Strategy

Liu,

Luo,

Wang

et al. 2024

IJGI

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show abstract

“…In the last few years, many researchers have tried to improve the cellular automata land-use change model and applied new methods to auto-calibrate these models [22,24]. Cellular automata have also been combined with agent-based modeling (ABM, [25]).…”

Section: Introductionmentioning

confidence: 99%

“…Individually based models, with simple behavioral models, have even made it possible to model individual land-use change decisions and to simulate their collective effects at higher geographical scales [26][27][28]. These kinds of models have been widely applied to model urban growth and land use and land-cover changes [24,[29][30][31]. They have also been applied in natural risk assessment [19] in the highland and mountainous areas of northern Vietnam [32,33], which have different environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

A Land-Use Change Model to Support Land-Use Planning in the Mekong Delta (MEKOLUC)

Truong

Nguyen

Tatsumi

et al. 2022

Land

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Agricultural land-use changes pose challenges for land managers in terms of ensuring the implementation of local land-use plans. This paper aims to build a land-use change model named MEKOLUC (Mekong Delta land-use change) for simulating land-use changes under the impacts of socioeconomic factors (profitability of land-use types, societal impacts on neighborhoods) and environmental factors (soil, salinity, persistence of salinity). The salinity diffusion map was generated using GAMA software and employed Markov cellular automata to depict the spread of salinity under the influence of dike and sluice gate system operations. The land-use decision-making process was based on multi-criteria selection of the main factors, which were land suitability, land convertibility, density of land use in the neighborhood and profitability of land-use patterns. The input data for the case study were historical land-use maps from 2005, 2010 and 2015 of Soc Trang, a coastal province in the Mekong Delta. The model was calibrated using a land-use map from 2010 (with kappa = 0.86) and was verified with land-use maps from 2015 and 2020 with deviations from 0 to 19%. The simulated results showed that shrimp–rice farming areas have been shrinking, even though these are recommended as sustainable farming systems. Inversely, intensive rice crops tended to change to rice–vegetable crops, vegetable crops or perennial fruit trees, which are projected to be well adapted to climate and salinity intrusion by 2030. This case study shows that the developed model is an essential tool for helping land managers and farmers build land-use plans.

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Real-time monitoring of sports performance based on ensemble learning algorithm and neural network

Zhou

Zhang

2023

Soft Comput

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In order to improve the performance of sports performance prediction, based on computational learning algorithms, this article builds a sports performance prediction model based on ensemble learning algorithms under the guidance of machine learning ideas. Moreover, this article applies the cascade principle to improve the accuracy of the model and determines the cascade structure, studies the characteristics of spatio-temporal sequence data and the modeling methods of spatio-temporal sequence models, and combines the idea of selective integration learning to improve the spatio-temporal neural network model. In addition, this paper uses the L1 regularization method to sparsely weight and combine multiple STELM models to achieve selective integration. Finally, this paper designs experiments to predict the performance of this model in sports performance prediction. The research results show that the prediction results of the sports performance prediction model constructed in this paper are accurate.

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Improving an Urban Cellular Automata Model Based on Auto-Calibrated and Trend-Adjusted Neighborhood

Cited by 9 publications

References 40 publications

VST-PCA: A Land Use Change Simulation Model Based on Spatiotemporal Feature Extraction and Pre-Allocation Strategy

VST-PCA: A Land Use Change Simulation Model Based on Spatiotemporal Feature Extraction and Pre-Allocation Strategy

A Land-Use Change Model to Support Land-Use Planning in the Mekong Delta (MEKOLUC)

Real-time monitoring of sports performance based on ensemble learning algorithm and neural network

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