Coupling Random Forest, Allometric Scaling, and Cellular Automata to Predict the Evolution of LULC under Various Shared Socioeconomic Pathways

Abstract

Accurately estimating land-use demand is essential for urban models to predict the evolution of urban spatial morphology. Due to the uncertainties inherent in socioeconomic development, the accurate forecasting of urban land-use demand remains a daunting challenge. The present study proposes a modeling framework to determine the scaling relationship between the population and urban area and simulates the spatiotemporal dynamics of land use and land cover (LULC). An allometric scaling (AS) law and a Markov (MK) chain are used to predict variations in LULC. Random forest (RF) and cellular automata (CA) serve to calibrate the transition rules of change in LULC and realize its micro-spatial allocation (MKCARF-AS). Furthermore, this research uses several shared socioeconomic pathways (SSPs) as scenario storylines. The MKCARF-AS model is used to predict changes in LULC under various SSP scenarios in Jinjiang City, China, from 2020 to 2065. The results show that the figure of merit (FoM) and the urban FoM of the MKCARF-AS model improve by 3.72% and 4.06%, respectively, compared with the MKCAANN model during the 2005–2010 simulation period. For a 6.28% discrepancy between the predicted urban land-use demand and the actual urban land-use demand over the period 2005–2010, the urban FoM degrades by 21.42%. The growth of the permanent urban population and urban area in Jinjiang City follows an allometric scaling law with an exponent of 0.933 for the period 2005–2020, and the relative residual and R2 are 0.0076 and 0.9994, respectively. From 2020 to 2065, the urban land demand estimated by the Markov model is 19.4% greater than the urban area predicted under scenario SSP5. At the township scale, the different SSP scenarios produce significantly different spatial distributions of urban expansion rates. By coupling random forest and allometric scaling, the MKCARF-AS model substantially improves the simulation of urban land use.

Assessing and Enhancing Predictive Efficacy of Machine Learning Models in Urban Land Dynamics: A Comparative Study Using Multi-Resolution Satellite Data

“…For each node in the tree, a random subset of the predictors (input data) is selected, and the best split among these predictors is chosen based on a measure like the Gini impurity or information gain. This process continues until the tree is fully grown and replicated across multiple such trees forming the "forest" [62,63]. In the context of land cover modeling, each decision tree in the forest represents a possible sequence of land-use changes based on different combinations of the input variables.…”

Analyzing and Simulating the Influence of a Water Conveyance Project on Land Use Conditions in the Tarim River Region

“…Numerous environmental and social issues are associated with rapid urbanization and population growth throughout the world [10][11][12][13]. In particular, arid and semi-arid areas suffer from a series of environmental problems, such as land degradation [14][15][16], desertification [17][18][19], and water scarcity [20][21][22], even though the urban growth rates are not as high as those in fast-growing regions. It is increasingly important to analyze and simulate the LULC changes in these areas, which can provide practical instructions for environmental management and decision making [23][24][25][26][27][28][29].…”