城市环境预测的量子混合框架,集成先进的人工智能和地理空间模拟
Janis Peksa1,2, Andrii Perekrest1,3, Kyrylo Vadurin3
1Information Technology Faculty, Turiba University, Graudu Street 68, LV-1058 Riga, Latvia.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
这项研究整合了用于环境预测的经典和量子数据分析,提高了环境监测的准确性. 开发的自适应系统改善了对城市空气污染和环境威胁的预测.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 量子计算是一种量子计算.
背景情况:
- 环境监测面临着预测准确性和处理复杂,小型数据集的挑战.
- 当前的建模技术与环境数据的高度分离性作斗争.
研究的目的:
- 为环境过程开发先进的预测和建模技术.
- 通过混合经典和量子方法提高环境参数预测的准确性.
- 解决环境监测中的关键问题,包括数据复杂性和预测可靠性.
主要方法:
- 深度神经网络 (例如,LSTM,变压器) 和经典算法 (AutoARIMA,BATS) 与量子计算方法的整合.
- 开发结合量子层的混合预测模型.
- 用空气污染模拟器应用空间插值技术和A*算法与高斯核.
主要成果:
- 实验验证证了拟议的混合经典和量子预测方法的有效性.
- 与传统方法相比,在预测环境参数方面表现出更高的准确性.
- 成功开发了一种适应性系统概念,用于预测城市地区的环境条件.
结论:
- 拟议的混合模型在环境预测准确性和适应性方面取得了重大进展.
- 开发的模型对环境威胁的操作监测和预测具有实用性.
- 结果适用于环境信息系统,以提高预测能力.
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