State of the Art on Artificial Intelligence in Land Use Simulation

Castro, Luz; Machado, Penousal; Santos, Iria; Rodríguez-Fernández, Nereida; Torrente-Patiño, Alvaro; Carballal, Adrián

doi:10.1155/2022/2291508

Cited by 4 publications

(2 citation statements)

References 98 publications

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“…The CA model comprises the cell, cell space, neighbor, time, and rule. The model describes the new pattern of LULC, considering the state of previous neighborhood cells [58,59]. The distance between the neighbor and the cell defines the weight factor of changing to a particular land cover.…”

Section: Lulc Prediction Using the Ca-markov Chain Modelmentioning

confidence: 99%

A CA–Markov-Based Simulation and Prediction of LULC Changes over the Nyabarongo River Basin, Rwanda

Gasirabo,

Xi,

Hamad

et al. 2023

Land

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Over the past few decades, the growth of population and the development of the economy have had a significant impact on the way land is used and covered (LULC) in the Nile Nyabarongo River basin. However, there is limited knowledge about the patterns of land use and the mechanisms that drive changes in these patterns because of human activities. Therefore, it is crucial to examine how land use and cover are shifting in this area, identify the factors responsible for these changes, and forecast future patterns. This study sought (1) to evaluate the changes in LULC from 1990 to 2020 and (2) to predict future fluctuations until 2060. By analyzing the LULC data for the years 1990, 2000, 2010, and 2020, this study simulated the future LULC patterns of the area for the next 30 years using the LULC transition matrix and the Markov chain model. The study classified the LULC into five categories: forestland, grassland, cropland, settlement, and water. The results revealed that there will be significant changes in the LULC of the study area from 2030 to 2060. The forest area is projected to decrease by 801.7 km, 771.6 km, and 508.2 km, while the cropland area will expand by 6307.2 km, 6249.2 km, and 6420.6 km during this period. The grassland area will experience a small increase of 761.1 km, 802.4 km, and 859.1 km, and the settlement area will also grow by 355.2 km, 407.4 km, and 453.2 km. In contrast, the water area will decrease by 55.9 km, 50.5 km, and 40 km. The ongoing pattern of LULCC is expected to persist over the next three decades, with an increase in cropland area and grassland. This study’s findings can provide valuable insights for land use planners and water resource managers in developing fair land use and water resource management policies for the entire region, enabling them to make well-informed decisions.

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Section: Lulc Prediction Using the Ca-markov Chain Modelmentioning

confidence: 99%

A CA–Markov-Based Simulation and Prediction of LULC Changes over the Nyabarongo River Basin, Rwanda

Gasirabo,

Xi,

Hamad

et al. 2023

Land

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“…In contrast, artificial intelligence (AI)-based methods offer the advantage of capturing the nonlinearity and heterogeneity of urban growth. Their improvement over traditional CA has achieved good results in urban growth simulations [19]. Many scholars have simulated land-use dynamics by combining artificial intelligence methods with CA models.…”

Section: Introductionmentioning

confidence: 99%

Simulating Urban Agglomeration Expansion in Henan Province, China: An Analysis of Driving Mechanisms Using the FLUS Model with Considerations for Urban Interactions and Ecological Constraints

Gao

Wang

Zhang

2023

Land

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Urban expansion is influenced by complex and variable social, economic, natural, and policy-related factors. Given their nonlinear interactions, accurately modeling these urban expansion processes poses a challenge. While most studies treat the city as an independent entity, prioritizing internal urban factors, urban land expansion is influenced by intercity interactions and the ecological environment. This study proposes a new approach that couples the gravitational field model, ecological constraints, and the Future Land Use Simulation (FLUS) model, comprehensively considering the impact of intercity interaction and the ecological environment. The experiment in Henan Province in China assessed the effects of factors such as basic spatial variables (Slope and distance to the city center), urban gravitational field, and ecological constraints on urban expansion through the optimal parameters-based geographical detector (OPGD) model. The feasibility of the method was confirmed by this case study, which shows that it improves the simulation accuracy of the urban agglomeration scale, particularly for central cities. We identified the urban gravitational field and ecological constraints as two important factors affecting the expansion of urban agglomerations. Areas with stronger urban spatial fields are more likely to attract neighboring resources and promote urban expansion, whereas ecological factors constrain the expansion behavior of cities under the condition of ecological and environmental resource protection needs, and both of them work together to influence the expansion behavior of urban clusters. Therefore, we posit that intercity interactions and ecological constraints are important considerations for the future spatial planning of urban agglomerations and for coordinating the harmonious development of urbanization and ecological conservation.

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Pixel-Level Projection of PM_2.5 Using Landsat Images and Cellular Automata Models in the Yangtze River Delta, China

Tang

Feng

Tong

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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State of the Art on Artificial Intelligence in Land Use Simulation

Cited by 4 publications

References 98 publications

A CA–Markov-Based Simulation and Prediction of LULC Changes over the Nyabarongo River Basin, Rwanda

A CA–Markov-Based Simulation and Prediction of LULC Changes over the Nyabarongo River Basin, Rwanda

Simulating Urban Agglomeration Expansion in Henan Province, China: An Analysis of Driving Mechanisms Using the FLUS Model with Considerations for Urban Interactions and Ecological Constraints

Pixel-Level Projection of PM_2.5 Using Landsat Images and Cellular Automata Models in the Yangtze River Delta, China

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State of the Art on Artificial Intelligence in Land Use Simulation

Cited by 4 publications

References 98 publications

A CA–Markov-Based Simulation and Prediction of LULC Changes over the Nyabarongo River Basin, Rwanda

A CA–Markov-Based Simulation and Prediction of LULC Changes over the Nyabarongo River Basin, Rwanda

Simulating Urban Agglomeration Expansion in Henan Province, China: An Analysis of Driving Mechanisms Using the FLUS Model with Considerations for Urban Interactions and Ecological Constraints

Pixel-Level Projection of PM2.5 Using Landsat Images and Cellular Automata Models in the Yangtze River Delta, China

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Product

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Pixel-Level Projection of PM_2.5 Using Landsat Images and Cellular Automata Models in the Yangtze River Delta, China