通过改进的遗传算法来提高反向传播神经网络的准确性,用于在生态村的旅游流预测
Xiaolong Chen1, Cora Un In Wong1, Hongfeng Zhang2
1Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, 999078, China.
Scientific reports
|October 15, 2025
概括
一个新的自适应多种群遗传算法反向传播 (AMGA-BP) 模型提高了旅游流预测的准确性. 这种先进的方法为可持续旅游管理提供了可靠的工具,即使在具有挑战性的条件下也是如此.
科学领域:
- 旅游管理 旅游管理
- 人工智能的人工智能
- 生态规划 生态规划
背景情况:
- 传统的旅游流预测模型,如逆向传播神经网络 (BP-NN) 和遗传算法逆向传播神经网络 (GABP-NN),与非线性复杂性和外部因素作斗争.
- 预测旅游流动对于有效的资源管理和可持续的旅游发展至关重要,特别是在生态敏感地区.
研究的目的:
- 开发一种先进的预测模型,即自适应多种群遗传算法逆向传播 (AMGA-BP),以克服现有方法的局限性.
- 提高旅游流预测在各种条件下的准确性和稳定性,包括高峰季节和恶劣天气.
主要方法:
- 提出了一个新的自适应多种群遗传算法反向传播 (AMGA-BP) 模型.
- 实现了一种独特的双层阶梯结构染色体设计,用于同时优化网络结构和重量.
- 验证了模型的性能与传统的BP,GA-BP,LSTM和随机森林模型相比.
主要成果:
- AMGA-BP实现了5.32%的平均绝对百分比误差 (MAPE) 和0.9869.2的r2.
- 显著超过BP (25.22%的MAPE) 和GA-BP (13.61%的MAPE) 的表现.
- 在高峰季节 (6.00% MAPE) 和恶劣天气 (5.50% MAPE) 中表现出强大的准确性,超过了LSTM (8.20% MAPE) 和随机森林 (9.80% MAPE).
结论:
- 该AMGA-BP模型为旅游流预测提供了卓越的性能和可靠性.
- 为旅游经理提供有价值的工具,以支持在班梁古村等地区的可持续发展和资源管理.
- 解决了旅游流动性预测中非线性复杂性和模糊决策的挑战.
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