Abstract
Rapid urban expansion has seriously threatened ecological security and the natural environment on a global scale, thus, the simulation of dynamic urban expansion is a hot topic in current research. Existing urban expansion simulation models focus on the mining of spatial neighborhood features among driving factors, however, they ignore the over-fitting, gradient explosion, and vanishing problems caused by the long-term dependence of time series data, which results in limited model accuracy. In this study, we proposed a new dynamic urban expansion simulation model. Considering the long-time dependence issue, long short term memory (LSTM) was employed to automatically extract the transformation rules through memory units and provide the optimal attribute features for cellular automata (CA). This study selected Lanzhou, which is a semi-arid region in Northwest China, as an example to confirm the validity of the model performance using data from 2000 to 2020. The results revealed that the overall accuracy of the model was 91.01%, which was higher than that of the traditional artificial neural network (ANN)-CA and recurrent neural network (RNN)-CA models. The LSTM-CA framework resolved existing problems with the traditional algorithm, while it significantly reduced complexity and improved simulation accuracy. In addition, we predicted urban expansion to 2030 based on natural expansion (NE) and ecological constraint (EC) scenarios, and found that EC was an effective control strategy. This study provides a certain theoretical basis and reference value toward the realization of new urbanization and ecologically sound civil construction, in the context of territorial spatial planning and healthy/sustainable urban development.
Highlights
The rapid population growth, social economic development, and the continuous expansion of urban built-up land have led to an array of significant challenges on a global scale, such as the aggravation of climate change, depletion of natural resources, and the degradation of the ecological environment [1,2]
Built-up land increased from 229.27 km2 in 2000 to 757.76 km2 in 2020, which translates to a 3.31 fold increase in urbanized area at an annual growth rate of 23.05%
From 2000 to 2010, the study area was at a stage of slow expansion, whereas from 2010 to 2020, it experienced a stage of rapid expansion with an annual growth rate of 28.23%, where the total built-up area increased by 559.54 km2
Summary
The rapid population growth, social economic development, and the continuous expansion of urban built-up land have led to an array of significant challenges on a global scale, such as the aggravation of climate change, depletion of natural resources, and the degradation of the ecological environment [1,2]. Contradictions between urban expansion and ecological environmental degradation have become increasingly severe [3,4]. The growth rate of urban built-up land in the 21st century is 2.14 times that of the. High-value ecological land, such as cultivated land, forests, and water body, is faced with irreversible risk of being quickly converted to built-up land [6].
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