ANN-CA Model Construction under SD Constraint: A Case Study on the Land-use Change Trend in Ya'an City

Land systems are closely related to various factors, including social issues, national economies, natural environments, etc. However, the dynamic evolution simulation of land systems is quite complex. There are many problems in terms of land use in China, such as extensive development, significant regional differences, and imbalanced development. From this perspective, it is imperative to study systematic land use evolution according to local contexts. In this study, two methods that are commonly used to solve complexity issues— “top-down” system dynamics and “bottom-up” cellular automata—were combined to investigate the land use evolutionary trend in Ya’an City. Firstly, a system dynamics model of land resource use was constructed based on the causal relationship between land and macroscopic elements, and the quantitative future predictions of land use structures in Ya’an were obtained. Next, an ANN-CA model containing three steps—artificial neural network training, optimization of cellular automata parameters, and model calibration—was constructed. The land use scenario in Ya’an City in 2018 was simulated and the quantity and space accuracy verification were conducted. Finally, the spatial layout of land use in Ya’an City in 2028 and 2038 was predicted, and the land use evolution trend was studied using the model under the constraint of the quantitative prediction results of system dynamics. The results demonstrated the good simulation effect of the ANN-CA model under the SD constraint. The overall simulation accuracy of the model is the highest (93.93%) when the threshold of transformation is 0.8 and the diffusion coefficient is 1. In the future, the spatial distribution of different land types in Ya’an City will change slightly, and construction land presents an expansion law from the center to surrounding areas.

Land use is an important link between human socioeconomics and natural environmental evolution. Land use changes reflect the various activities of human beings, climate change, and ecological environmental changes, to a large extent. Humans’ understanding of land use changes has significantly developed in recent years (Aurélien et al.2012; Deng X.2011). Studies on land use have received further impetus since two large international organizations (IGBP and IHDP) have promoted research plans on “land use/coverage changes” in 1995. Currently, Chinese scholars have mainly investigated land use by using different research methods according to different research objectives of dynamic change analysis, the driving mechanism, and simulation predictions (Zhao et al.2016). With improving analyses on dynamic changes and studies on their driving mechanisms, the simulation of land use changes has developed rapidly. The relevant technological means have also become increasingly mature. Methods to simulate land use changes have developed from the earliest quantitative methods, which simply predict land use demand, to current spatial methods that simulate and rebuild the spatial layout of land use (Qian et al.2020).

Quantitative methods generally include system dynamics (SD), gray models (GMs), Markov models (Markov), artificial neural networks (ANNs), and the computable general equilibrium of land use change (CGELUC) (Rasmussen et al.2012; Liu et al.2021; Silburn et al. 2011; Al-Shaar et al. 2021; Lpes et al. 2020). Relevant studies have demonstrated that the abovementioned methods mainly build models based on historical experience, during which the model has to be calibrated continuously to achieve high-accuracy prediction results (Koko et al. 2020). In comparison to other methods, the SD model is applicable to many fields of study. It can not only predict land use demand in a region, but also understand the feedback mechanism in the system, which is remarkably advantageous (Xu and Luo 2018). In contrast to quantitative methods, spatial methods focus more on the prediction of the spatial structural layout of land use (Park et al.2011). Common spatial methods include the change of land use and effect (CLUE) model, dynamics of land system (DLS) model, cellular automata (CA) model, and multi-agent (MAS) model (Wang et al. 2019; Jin et al.2019; Almeida et al.2008; Xu et al. 2020). Among them, CA has proven to be an effective and convenient means to simulate urban dynamics and land use change. As a high-resolution microscale spatial dynamic model, the geographical CA model can easily be integrated with GIS spatial data and can reflect dynamic changes in spaces directly (Espíndola et al. 2021).

In terms of land use, cellular automata have been used to determine the model structure, set the transformation rules, and couple different models. White et al. built a CA rational model structure to understand urban land expansion evolution (White and Engelen 1994). Li et al. discussed how to integrate CA with GIS and geographic information systems in cities and farmlands (Li and Yeh 2000). Transformational rules are the core of CA modeling; initially, the transformation rules for CA were set according to experience, as was the case with CLUE-S. Subsequently, CA was coupled with other methods to simplify the definitions of the complex transformation rules. For example, Li and Yeh introduced principal component analysis and neural networks into CA modeling in 2002 (Yeh and Li 2002; Li et al. 2002). Later, they guided teams to continue to investigate CA modeling and established the theory of geographical simulation systems. Subsequently, they even coupled logic regression methods, decision trees, and CA, designing the geographical simulation optimization system (GeoSOS) and publishing an open-source version of the software in 2018. This software has been extensively used by scholars worldwide (Li et al.2011). Moreover, Liu Xiaoping et al. added the ant colony algorithm, artificial immune system, system dynamics, mixed particles, and double-integrated Kalman filter into the CA model (Liu et al.2008; Liu et al.2010; Huang et al.2013 Liu et al.2012; Ai L B 2013; Liu et al. 2015; Liu et al. 2017).

The issue of land use change is multi-disciplinary. Not only do human activity elements such as society, humanity, economy, and industrial structure have to be considered, but natural elements like the terrain and environment must also be accounted for. Therefore, it can be viewed as a complex systems engineering problem (Gao et al. 2021; Zhao et al. 2022). Therefore, this study combined two representative methods for addressing processing complexity, namely, the “top-down” system dynamics (SD) method, which describes variables from the objective level, and the “bottom-up” cellular automata (CA) method, which facilitates dynamic evolution from the framework of methods. In this process, artificial neural networks were added to simplify the transformation rules of CA, and a model was constructed to explore the land use evolutionary trend in the study area. The constructed model can not only couple macroscopic factors and microscopic factors, which influence land use changes, but also unify the quantity level and spatial level in the prediction method. It forms a basis to further understand relevant theory and supplements current research on land use.

In this section, the ANN-CA model that passed the accuracy verification was used to simulate and predict the land use scenario in Ya’an City in the future, facilitating the analysis of the future land use evolutionary trend in the study area. Firstly, a hypothesis was proposed for the model, and simulation was performed under the premise of no significant changes in humanity, society, economy, and nature. Secondly, constraints against model operation were set and the ecological factor chart of Ya’an City was input into the model. A constraint that the land use types within the scope of a natural reserve are prohibited for transformation into construction land and cultivated land was set to protect the ecological environment in Ya’an City. Thirdly, the data of spatial influencing factors was input, and the neural network model was constructed based on land use data in 2008 and 2018 for training and extracting relevant laws. The CA parameters were set to simulate and predict land use situations in Ya’an City in 2028; the model was calibrated spatially and quantitatively. The offset between the calculated actual spatial centers of different land use types in 2018 and the calculated simulation space centers in 2028 was within the allowable range. Moreover, the land use prediction results in Ya’an City in 2028 were output when the errors between the quantitative prediction area based on the SD model and the simulation areas based on the ANN-CA model of four land use types (cultivated land, construction land, forest land, and grass land) in 2028 were lower than ± 5%. Finally, data of the spatial influencing factors, actual land use data in 2018 and simulated land use data in 2028, which were output in the previous step, were input into the model. A neural network model was constructed and trained to extract relevant laws. CA parameters were set to simulate and predict land use scenarios in Ya’an City in 2038. The model was calibrated spatially and quantitatively. The land use prediction results in Ya’an City in 2039 were output when the offset between the calculated spatial centers of different land use types in 2028 and 2038 was within the allowable range, and the error range between the quantitative prediction area based on the SD model and the simulation areas based on ANN-CA model of four land use types (cultivated land, construction land, forest land, and grass land) in 2038 was lower than ± 5%. The land use prediction results in 2028 and 2038 are shown in Fig. 9.

The system model in this study also simulated and predicted the evolution of urban construction land in Ya’an City. Urban construction land includes urban industrial land, urban tertiary industrial land, urban residential land, urban road and traffic facility land, and others. These urban construction land types increased from 851, 818, 989, 900, and 543 ha. in 2019 to 1870, 2872, 1053, 1552, and 1700 ha. in 2038, respectively. The area of urban tertiary industrial land in 2038 will be the largest, which agrees well with the predicted tertiary industrial development in 2038. The land use prediction results in 2038 are shown in Fig. 10–12. The prediction of different land use types in urban construction land can provide some references for urban development planning and policy formulation in Ya’an City.

The actual land use status in Ya’an City in 2018 was compared with land use simulation results in 2028 and 2038. It can be seen that the spatial location distributions of different land use types change slightly, indicating that the spatial center migration analytical method can calibrate the model very well. The prediction results of four land use types in Ya’an City in 2028 and 2038 based on the SD model were compared with simulation results based on the ANN-CA model, and the error was calculated. The results are shown in Table 4. Obviously, under the constraint of calculated results based on the SD model, the absolute error between simulation results based on the ANN-CA model and the predicted area based on the SD model of cultivated land, construction land, forest land, and grass land was lower than 5% in both 2028 and 2038.

For better observation of the construction land expansion trend in Ya’an City, the construction land was extracted from the land use simulation results in 2028 and 2038 based on the chart of the land use status in 2018. The construction lands were stacked from top to bottom in 2018, 2028, and 2038, the results of which are shown in Fig. 13.

In Fig. 13, the yellow region represents the construction land area in Ya’an City in 2018, the blue region represents the construction land expansion area from 2018 to 2028, and the red region represents the construction land expansion area from 2028 to 2038. It is clear that the construction land in the central and eastern regions (Tianquan County, Lushan County, Yucheng County, and Mingshan District) and southern regions (Hanyuan County) expanded significantly. The regions with great expansion of construction land are mainly distributed along rivers and roads, fully reflecting the driving and radiation effect of roads and rivers on urban construction in Ya’an City. To inform decisions regarding the traffic road network in Ya’an City, attention was paid to providing theoretical references for the implementation of policies like water resource management and protection. Generally, the construction land in Ya’an City presented a law of expansion from the center to surrounding areas, which was consistent with the open development pattern of “Four-way expansion” proposed by Ya’an City during the 13th Five-year plan. This confirms that the simulated evolutionary results of the ANN-CA model under the SD constraint are relatively accurate and reasonable. They can provide planning departments with reliable references to formulate related policies.

In this study, SD and CA, which are common methods to solve complexity problems, are combined to construct a model that simulates the land use evolutionary trend in Ya’an City. Results demonstrate that (1) when setting the CA parameters, under four parameter combinations with relatively high accuracy of fitting, the overall simulation accuracy of the model is the highest when the threshold of transformation is 0.8 and the diffusion coefficient is 1, reaching 93.93%. (2) When verifying the simulation accuracy, it can be seen from the collaborative analysis of the verification of quantitative and spatial accuracy that the ANN-CA model achieves relatively good simulation results. (3) It is found, by studying the land use evolutionary trend in the study area, that the construction land in Ya’an City from 2018 to 2038 presents an expansion law from the center to surrounding areas, which agrees well with the open development pattern of “Four-way expansion” in the study area. This confirms that the simulated evolutionary results of the ANN-CA model under the SD constraint can provide planning departments with reliable information to formulate related policies.

In this study, an ANN-CA model under the SD constraint is built and used to predict the spatial evolution of land use in Ya’an City. Although this study has achieved significant results in terms of model construction and simulation, there is still space for further improvement: (1) Influencing factors of different levels, including population, economy, terrain, river, road, and ecological factors, are used; the land use change laws of the study area are extracted; and the spatial simulation of the model is constrained. As a result, the simulation results of the ANN-CA model reach the expected effect. However, this study ignores some other factors that influence land use changes, such as climate, policy, and regulations. These variables should be integrated into the model in the future. (2) This study focuses on providing a tool to study the dynamic changes in land use and investigates the prediction and simulation of land use changes in Ya’an City. However, the prediction content has not been analyzed thoroughly. In the future, land use changes in Ya’an City should be simulated under different scenarios.

Data availability statement

Data and code will be made available on reasonable request.

Funding statement

This research was supported by the National Natural Science Foundation of China (52072066) & Jiangsu Province Science Fund for Distinguished Young Scholars (BK20200014)..

Conflict of interest disclosure

There is no conflict of interest associated with this research work.

Ethics approval statement

This article does not contain any studies with human participants performed by any of the authors.

Declaration of competing interest

The author declares that there is no conflict of interest associated with the research work.

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