生物多样性和旅游治理的人工智能:来自亚马逊地区多层感知模型的预测见解
Jessie Bravo-Jaico1, Oscar Serquén1, Roger Alarcón1
1Digital Transformation Research Group, National University Pedro Ruiz Gallo, Lambayeque, Peru.
Frontiers in artificial intelligence
|February 23, 2026
概括
人工智能使用多层感知神经网络 (MLP) 可以准确预测生物多样性热点的旅游需求. 这有助于适应性管理,以实现流行后脆弱生态系统的可持续恢复和保护.
科学领域:
- 生态旅游是生态旅游的一部分.
- 保护科学 保护科学
- 人工智能的人工智能
背景情况:
- COVID-19大流行严重扰乱了生物多样性丰富地区的旅游业,危及自然和文化遗产.
- 由于缺乏适应性管理工具,秘鲁亚马逊巴古亚省在可持续旅游恢复方面面临挑战.
研究的目的:
- 将人工智能 (AI) 与生物多样性保护相结合,以实现可持续旅游复苏.
- 利用人工智能在脆弱的生态系统中开发旅游治理的预测模型.
主要方法:
- 多层感知器 (MLP) 神经网络的应用.
- 两十年 (2003-2023) 国内游客数据的分析.
- 预测模型的评估,包括和没有大流行影响的数据 (2020年).
主要成果:
- 多项计划模型准确地捕捉到访客动态和预测需求波动.
- 预测能力使适应性规划成为可能,以防止像Tsunsuntsa布这样的地点的过度旅游.
- 人工智能展示了作为弹性治理工具的战略潜力.
结论:
- 人工智能,特别是神经网络,为协调社会经济复苏和生物多样性保护提供了可操作的见解.
- 该研究为脆弱地区的目的地可持续管理提供了一个可重复的模型.
- 将计算方法集成到旅游治理中,将技术和保护联系起来,以实现长期可持续性.
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