A Dynamic Urban Lake Area Evolution Model Based on Multilevel Grid, Cellular Automata, and Multiagent System

Zhu, Jianfeng; Tian, Shenzhen

doi:10.1155/2020/1845090

Cited by 2 publications

(2 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With the development of artificial intelligence (AI) technology, the study of a joint model of MAS and CA has become a hot spot for urban space evolution simulation. This type of model can simulate the influences of macro factors (e.g., natural environment, geographical location, policy) and micro factors (e.g., different urban agents) on urban space evolution [ 38 ]. Therefore, the joint model of MAS and CA has a stronger ability to simulate urban space than other models.…”

Section: Discussionmentioning

confidence: 99%

Simulation and Analysis of Urban Production–Living–Ecological Space Evolution Based on a Macro–Micro Joint Decision Model

Tao

Wang

Zou

2021

IJERPH

View full text Add to dashboard Cite

The precise simulation of urban space evolution and grasping of the leading factors are the most important basis for urban space planning. However, the simulation ability of current models is lacking when it comes to complicated/unpredictable urban space changes, resulting in flawed government decision-making and wasting of urban resources. In this study, a macro–micro joint decision model was proposed to improve the ability of urban space evolution simulation. The simulation objects were unified into production, living and ecological space to realize “multiple planning in one”. For validation of the proposed model and method, remote sensing images, geographic information and socio-economic data of Xuzhou, China from 2000 to 2020 were collected and tested. The results showed that the simulation precision of the cellular automata (CA) model was about 87% (Kappa coefficient), which improved to 89% if using a CA and multi-agent system (MAS) joint model. The simulation precision could be better than 92% using the prosed model. The result of factor weight determination indicated that the micro factors affected the evolution of production and living space more than the macro factors, while the macro factors had more influence on the evolution of ecological space than the micro factors. Therefore, active policies should be formulated to strengthen the ideological guidance towards micro individuals (e.g., a resident, farmer, or entrepreneur), and avoid disordered development of living and production space. In addition, ecological space planning should closely link with the local environment and natural conditions, to improve urban ecological carrying capacity and realize urban sustainable development.

show abstract

Section: Discussionmentioning

confidence: 99%

Simulation and Analysis of Urban Production–Living–Ecological Space Evolution Based on a Macro–Micro Joint Decision Model

Tao

Wang

Zou

2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…[White, 2006] introduces a CA with variable grid size to account for heterogeneities across scales. [Zhu and Tian, 2020] couple an agent-based model with a CA at multiple scales. [Yu et al, 2018] embed a local CA into a regional intercity model and a macroscopic potential model.…”

Section: Introductionmentioning

confidence: 99%

Strong coupling between scales in a multi-scalar model of urban dynamics

Raimbault

2021

Preprint

View full text Add to dashboard Cite

Urban evolution processes occur at different scales, with intricate interactions between levels and relatively distinct type of processes. To what extent actual urban dynamics include an actual strong coupling between scales, in the sense of both top-down and bottom-up feedbacks, remains an open issue with important practical implications for the sustainable management of territories. We introduce in this paper a multi-scalar simulation model of urban growth, coupling a system of cities interaction model at the macroscopic scale with morphogenesis models for the evolution of urban form at the scale of metropolitan areas. Strong coupling between scales is achieved through an update of model parameters at each scale depending on trajectories at the other scale. The model is applied and explored on synthetic systems of cities. Simulation results show a non-trivial effect of the strong coupling. As a consequence, an optimal action on policy parameters such as containing urban sprawl is shifted. We also run a multi-objective optimization algorithm on the model, showing showing that compromise between scales are captured. Our approach opens new research directions towards more operational urban dynamics models including a strong feedback between scales.

show abstract

A Dynamic Urban Lake Area Evolution Model Based on Multilevel Grid, Cellular Automata, and Multiagent System

Cited by 2 publications

References 39 publications

Simulation and Analysis of Urban Production–Living–Ecological Space Evolution Based on a Macro–Micro Joint Decision Model

Simulation and Analysis of Urban Production–Living–Ecological Space Evolution Based on a Macro–Micro Joint Decision Model

Strong coupling between scales in a multi-scalar model of urban dynamics

Contact Info

Product

Resources

About