多重时空分辨率对城市增长模拟模型性能的影响
Tingting Xu1,2, Heng Su1,2, Biao He2,3
1Chongqing University of Posts and Telecommunications, Chongqing, China.
iScience
|January 1, 2024
概括
卷积长短期记忆 (ConvLSTM) 模型通过识别理想的空间和时间分辨率来优化城市增长模拟. 这一框架提高了城市扩张建模的预测准确性.
科学领域:
- 城市规划和地理学
- 地理空间数据科学数据科学
- 环境科学中的人工智能
背景情况:
- 城市增长模拟模型对于理解和预测土地使用变化至关重要.
- 选择适当的空间和时间分辨率对于准确的城市扩张建模至关重要.
- 现有的模型往往缺乏系统的方法来确定最佳的时空分辨率.
研究的目的:
- 开发一个框架来研究各种空间和时间分辨率对城市增长模拟的影响.
- 确定城市扩张模型的最佳时空分辨率,以梁江新区为案例研究.
- 评估卷积长短期记忆 (ConvLSTM) 模型与传统模型的性能.
主要方法:
- 利用卷积式长期短期记忆 (ConvLSTM) 模型和三个常规模型.
- 使用2009年至2017年的城市数据来模拟2018年的城市增长.
- 测试了多个空间分辨率 (例如,90m) 和时间分辨率 (例如,两年数据包含).
主要成果:
- 与传统模型相比,ConvLSTM模型表现出优异的模拟性能.
- 发现最优的时间分辨率是包括前两年的数据.
- 90米的最佳空间分辨率,两年时间步骤和100x100空间波器,产生了最高的精度 (卡帕值为0.87).
结论:
- 输入数据的特点显著影响城市增长模拟结果.
- 通过精心选择的时空分辨率,ConvLSTM模型提供了准确的城市扩张预测.
- 开发的框架为优化城市增长模拟过程提供了有价值的见解.
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