城市扩展极限学习机模型

城市空间结构及其扩展的模拟是城市科学管理与规划的重要前提，本文基于极限学习机提出了顾及不同非城市用地转化为城市用地差异与强度的城市扩展元胞自动机模型（ELM-CA）。模型验证表明：①ELM-CA模型的模拟精度达到70.30%，相比于逻辑回归和神经网络分别提高了2.21%和1.54%，FoM系数分别提高了0.025 9和0.017 9，Kappa系数分别提高了0.024 7和0.016 9，且Moran I指数接近于实际值，说明极限学习机模型较逻辑回归和神经网络能更有效模拟城市扩展的空间形态及其变化；②ELM模型的训练时间仅为神经网络的1/3左右，体现了ELM学习速度的优势；③在小样本情况下，逻辑回归和神经网络都受到明显的影响，而极限学习机还能保持良好的性能，这个特点使其在样本难以获取的情况下具有明显的优势。两个时相的城市扩展模拟与真实数据的比较表明：基于极限学习机的城市扩展元胞自动机模型（ELM-CA），简化了CA模型的复杂度，并在小样本情况下能有效提高模拟精度，适合于复杂土地利用条件下城市扩展模拟与预测。

Urban Expansion Model Based on Extreme Learning Machine

Urban space structure and its simulation are important prerequisites for urban scientific management and planning. Based on the extreme learning machine, this paper proposes an urban extended cellular automaton model (ELM-CA) that takes into account the differences and intensities of different non-urban land conversions into urban land use. The experimental results show that the urban simulation accuracy of ELM-CA model reaches 70.30%, which is 2.21% and 1.54% higher than logistic regression and neural network respectively. The FoM coefficient is increased by 0.025 9 and 0.017 9 respectively, and the Kappa coefficient is improved by 0.024 7 and 0.016 9 respectively. And the Moran I index is close to the actual value, which shows that the extreme learning machine model can simulate and predict the spatial shape and change of urban expansion more effectively than logistic regression and neural network; the training time of ELM model is only about 1/3 of the neural network, it reflects the advantage of ELM learning speed; In the small sample case, both logistic regression and neural network are significantly affected, and the extreme learning machine can maintain good performance, which makes it have obvious advantages when the sample is difficult to obtain. The comparison between urban expansion simulation and real data of two phases shows that the urban extended cellular automata model (ELM-CA) based on the extreme learning machine simplifies the complexity of the CA model and can effectively improve simulation accuracy under small sample conditions. The proposed model is suitable for urban expansion simulation and prediction under complex land use conditions.