Examining Land-Use Change Trends in Yucheng District, Ya’an City, China, Using ANN-CA Modeling

A Two-Layer SD-ANN-CA Model Framework for Multi-Typed Land Use and Land Cover Change Prediction under Constraints: Case Study of Ya’an City Area, Western China

“…The kappa coefficient of the simulation result of 2018 was 0.973, indicating that the model exhibited high predictive performance. We also tested the constructed ANN-CA model in our earlier work [49] for comparison, which is designed for four land-use types in the Yucheng district, Ya'an City. The results showed that when considering the whole city area and all six types of land use, there was an accuracy drop-down for the ANN-CA model and a remarkably higher accuracy for the SD-ANN-CA model, especially with forest land, grassland, and construction land.…”

“…Also, several parameters had to be set before the training progress began. According to earlier training results of the traditional ANN-CA model we applied to one district of Ya'an City, the ANN training network performed well at an e-learning rate of 0.05 and iterations of 100 [49]. Considering the expansion of the simulation area in this study, the e-learning rate was set to 0.05, and the max iteration was set to 200 to test both training accuracy and efficiency.…”

“…Secondly, considering land use competition under the Chinese rapid urbanization context with limited land resources and high population pressure, micro-level spatial constraints are also needed to improve accuracy in spatial layout. The results have been presented in the existing literature with better space pattern shapes, which are more similar to reality [48,49]; however, their effects have not been tested in a unified LUCC simulation model framework as essential steps.…”

“…During this time, spatial gravity center transfer analysis is also considered a micro-level spatial constraint for the final output results. It should be mentioned that this work is also further developed from our earlier preprint version, which can be found at the Research Gate [50]. The preprint can be considered as an attempt to introduce only macro-level demand constraint into the ANN-CA layer.…”

The suitability assessment for land territorial spatial planning based on ANN-CA model and the Internet of Things

“…However, applying IoT data to the field of land territorial spatial planning requires the establishment of effective models and analytical methods. In this context, researchers have explored models that combine the Artificial Neural Network (ANN) with Cellular Automata (CA) to simulate and evaluate urbanization processes with greater granularity [ [10] , [11] , [12] ]. It is now feasible to better comprehend the dynamics of urban evolution, resource distribution, environmental changes, and more by exploiting real-time data gathered from IoT and combining models such as ANN and CA.…”