ABSTRACT Abstract: When modelling urban expansion dynamics, cellular automata models focus mostly on the physical environments and cell neighbours, but ignore the ‘human’ aspect of the allocation of urban expansion cells. This limitation is overcome here using an intelligent self-adapting multiscale agent-based model. To simulate the urban expansion of Auckland, New Zealand, a total of 15 urban expansion drivers/constraints were considered over two periods (2000–2005, 2005–2010). The modelling takes into consideration both a macro-scale agent (government) and micro-scale agents (residents of three income levels), and their multi-level interactions. In order to achieve reliable simulation results, ABM was coupled with an artificial neural network to reveal the learning process and heterogeneity of the multi-sub-residential agents. The ANN-ABM accurately simulated the urban expansion of Auckland at both the global and local scales, with kappa simulation value at 0.48 and 0.55, respectively. The validated simulation result shows that the intelligent and self-adapting ANN-ABM approach is more accurate than an ABM with a general type of agent model (kappa simulation = 0.42) at the global scale, and more accurate than an ANN-based CA model (kappa simulation = 0.47) at the local scale. Simulation inaccuracy stems mostly from the outdated master land use plan.

中文翻译：

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新西兰奥克兰的城市扩张：基于智能自适应多尺度代理模型的 GIS 模拟

摘要 摘要：在对城市扩张动力学建模时，元胞自动机模型主要关注物理环境和细胞邻居，但忽略了城市扩张细胞分配的“人”方面。使用智能自适应多尺度基于代理的模型在这里克服了这一限制。为了模拟新西兰奥克兰的城市扩张，在两个时期（2000-2005、2005-2010）共考虑了 15 个城市扩张驱动因素/限制因素。该建模同时考虑了宏观代理（政府）和微观代理（三个收入水平的居民）及其多层次的相互作用。为了获得可靠的模拟结果，ABM 结合人工神经网络来揭示多子住宅代理的学习过程和异质性。ANN-ABM 在全球和局部尺度上准确模拟了奥克兰的城市扩张，kappa 模拟值分别为 0.48 和 0.55。经验证的仿真结果表明，智能自适应 ANN-ABM 方法在全球范围内比具有通用类型代理模型（kappa 模拟 = 0.42）的 ABM 更准确，并且比基于 ANN 的 CA 模型更准确（kappa 模拟 = 0.47）在局部范围内。模拟不准确主要源于过时的土地利用总体规划。42）在全球范围内，并且比基于 ANN 的 CA 模型（kappa 模拟 = 0.47）在局部范围内更准确。模拟不准确主要源于过时的土地利用总体规划。42）在全球范围内，并且比基于 ANN 的 CA 模型（kappa 模拟 = 0.47）在局部范围内更准确。模拟不准确主要源于过时的土地利用总体规划。