城市变化检测:使用机器学习和谷歌地球引擎评估生物物理驱动因素
Olufemi Sunday Durowoju1,2, Rotimi Oluseyi Obateru3, Samuel Adelabu4
1Department of Geography, University of the Free State, Bloemfontein Campus, Bloemfontein, South Africa. olufemidurowoju@gmail.com.
Environmental monitoring and assessment
|March 20, 2025
概括
这项研究使用机器学习来分析1987-2020年尼日利亚卡杜纳河流域的城市增长. 由于人口密度和水压力,城市迅速扩张,影响了土地覆盖面.
科学领域:
- 环境科学,城市规划,地理空间分析.
- 遥感和机器学习应用程序
- 可持续发展研究 可持续发展研究
背景情况:
- 由于人口增长,经济发展和政策转变,城市地区正在经历快速转型.
- 监测城市动态及其环境相互作用对于可持续规划至关重要.
- 尼日利亚的卡杜纳河流域 (KRB) 面临着重大的城市转型挑战.
研究的目的:
- 通过机器学习研究KRB的城市动态及其与生物物理条件的相互作用.
- 分析1987年至2020年之间的城市转型,并纳入环境变量.
- 确定城市变化的关键驱动因素,并为可持续城市发展提供见解.
主要方法:
- 利用来自谷歌地球引擎的192点数据集进行城市过渡分析 (1987-2020).
- 采用机器学习模型 (随机森林,SVM,KNN,MARS) 在R.中进行十倍交叉验证.
- 包含生物物理变量,如人口密度,降水和表面温度,数据预处理包括KNN归算和一热编码.
主要成果:
- 随机森林模型在预测城市变化模式方面实现了80%的整体准确性.
- 观察到重要的土地覆盖变化:城市扩张154.81%,水体减少95.79%,植被增长174%.
- 确定人口密度和水压力指数是主要的城市变化驱动因素,气候因素也发挥着作用.
结论:
- 机器学习为监测和预测城市发展提供了强大的方法.
- 结果为KRB的城市转型过程提供了有价值的见解.
- 结果支持可持续城市增长和减轻不利环境影响的战略.
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