使用混合人工智能模型评估城市化对生物质密度的影响:一个案例研究
1Department of Software Engineering, Istanbul Topkapi University, 34087, Istanbul, Turkey. buketisler@topkapi.edu.tr.
Scientific reports
|October 7, 2025
概括
城市扩张影响了植被. 像DWT-LSTM这样的先进AI模型可以预测植被变化,帮助可持续的城市规划和环境管理.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 人工智能的人工智能
背景情况:
- 城市化在全球范围内显著改变了社会经济结构和生态系统.
- 可持续的城市规划需要综合环境管理方法.
- 土耳其的Fethiye面临着平衡城市发展与生态保护的挑战.
研究的目的:
- 调查城市扩张对土耳其Fethiye自然植被覆盖面的影响.
- 使用人工智能驱动的模型来预测未来的植被动态.
- 为可持续的土地利用战略提供数据驱动的见解.
主要方法:
- 利用了2013-2023年的时间序列数据,包括陆地表面温度 (LST) 和规范差异构建指数 (NDBI).
- 采用长期短期记忆 (LSTM) 网络来建模和预测到2032年的规范差异植被指数 (NDVI) 值.
- 开发并验证了一种混合离散波段变换与LSTM (DWT-LSTM) 模型的混合离散波段变换,在NDVI准确度上实现了9.1%的改进.
主要成果:
- 该DWT-LSTM模型证明了植被动态的增强预测准确性.
- 根据CORINE土地覆盖数据验证,证实模型的稳定性和通用性.
- 城市化指标与生态退化模式的量化联系.
结论:
- 先进的AI模型是预测城市化导致的生态变化的有效工具.
- 调查结果支持做出明智的决策,以减轻城市发展对环境的不利影响.
- 该研究为快速城市化的沿海地区开发可持续的土地利用战略提供了基础.
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