ABSTRACT

This study compares the performance of gradient boosting decision tree (GBDT), artificial neural networks (ANNs), and random forests (RF) methods in LUC modeling in the Seoul metropolitan area. The results of this study showed that GBDT and RF have higher predictive power than ANN, indicating that tree-based ensemble methods are an effective technique for LUC prediction. Along with the outstanding predictive performance, the DT-based ensemble models provide insights for understanding which factors drive LUCs in complex urban dynamics with the relative importance and nonlinear marginal effects of predictor variables. The GBDT results indicate that distance to the existing residential site has the highest contribution to urban land use conversion (30.4% of the relative importance), while other significant predictor variables were proximity to industrial and public sites (combined 32.3% of relative importance). New residential development is likely to be adjacent to existing residential sites, but nonresidential development occurs at a distance (about 600 m) from such sites. The distance to the central business district (CBD) had increasing marginal effects on residential land use conversion, while no significant pattern was found for nonresidential land use conversion, indicating that Seoul has experienced more population suburbanization than employment decentralization.

摘要

本研究比较了梯度提升决策树 (GBDT)、人工神经网络 (ANN) 和随机森林 (RF) 方法在首尔大都市区 LUC 建模中的性能。本研究结果表明GBDT和RF比ANN具有更高的预测能力，表明基于树的集成方法是一种有效的LUC预测技术。除了出色的预测性能外，基于 DT 的集成模型还为理解复杂城市动态中哪些因素驱动 LUC 以及预测变量的相对重要性和非线性边际效应提供了见解。GBDT 结果表明，距现有住宅用地的距离对城市土地利用转换的贡献最大（相对重要性的 30.4%），而其他重要的预测变量是靠近工业和公共场所（相对重要性的总和为 32.3%）。新住宅开发可能与现有住宅用地相邻，但非住宅开发发生在距离这些用地（约 600 m）处。到中央商务区（CBD）的距离对住宅用地转换的边际效应越来越大，而非住宅用地转换没有发现显着模式，表明首尔经历了更多的人口郊区化而不是就业分散化。