基于FII-LSTM的城市生态环境脆弱性预测方法的研究
1School of Economics and Management, Southwest Petroleum University, Chengdu 610500, China.
Ecotoxicology and environmental safety
|February 28, 2026
概括
一个新的碎形互 (FII) - 长短期记忆 (LSTM) 模型改善了城市生态脆弱性的预测. 这种方法通过考虑复杂系统的自我相似性来提高准确性,有助于环境风险管理.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 城市规划 城市规划
背景情况:
- 有效预测城市生态脆弱性对于风险管理至关重要.
- 现有的方法缺乏准确性,因为它们忽视了复杂的非线性系统中的自我相似特征.
研究的目的:
- 提出城市生态环境脆弱性的先进预测方法.
- 通过将碎形属性与深度学习相结合,提高预测准确度.
主要方法:
- 开发了一个碎形位 (FII) 模型,以生成描述数据分布的位点.
- 构建了一个长短期记忆 (LSTM) 神经网络,利用MSE损失和Adam优化器.
- 利用35个中国城市的多维数据,对模型培训和验证采用持久方法.
主要成果:
- 与现有方法相比,FII-LSTM模型显示出高预测准确性和稳定性.
- 验证了该模型在描述碎形插值曲线和数据分布状态方面的有效性.
- 成功预测了2021-2025年样本城市的城市生态脆弱性.
结论:
- FII-LSTM模型在城市生态脆弱性预测方面取得了重大进展.
- 该模型的准确性和稳定性使其成为环境风险管理和政策制定的宝贵工具.
- 调查结果支持为可持续城市发展和生态保护做出明智决策.
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