基于LSTM网络的冰雪旅游智能评估
Jun Li1, Hailong Yuan2,3, Xia Yu4
1International College of Culture and Tourism, Jilin International Studies University, Changchun, 130117, Jilin, China.
Scientific reports
|July 28, 2024
概括
这项研究通过物联网 (IoT) 和长短期记忆 (LSTM) 网络增强了对冰雪旅游的评估. 改进后的模型显示,在各种气候条件下,预测准确度得到了提高.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 旅游管理 旅游管理
背景情况:
- 物联网 (IoT) 为环境检测提供了先进的功能.
- 长短期记忆 (LSTM) 网络擅长时间序列预测,这对于动态环境评估至关重要.
研究的目的:
- 开发一个对冰雪旅游适宜性的智能评估模型.
- 整合物联网和LSTM以提高预测准确性和运营效率.
主要方法:
- 利用物联网进行环境数据收集.
- 应用LSTM网络进行时间序列分析和预测.
- 在各种气候条件下对精细模型的实证验证.
主要成果:
- 改进后的模型显示出卓越的预测准确性,在温和气候下,在四个季度内从88%增加到94%.
- 在温和,寒冷,潮湿和干旱的气候中观察到一致的高性能.
- 该模型实现了适度的运行效率,运行时间为30-33秒,CPU使用率为40-43%.
结论:
- 集成的物联网和LSTM模型显著改善了对冰雪旅游适宜性的评估.
- 该模型有效地平衡了预测精度和资源消耗.
- 这项研究为应用先进的人工智能在旅游评估中提供了有价值的框架.
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