开发一种基于物联网的安全洪水监测和预测系统,使用基于遗传算法的神经模糊网络
Hero Rafael Castillo Arante1, Edwin Sybingco1, Maria Antonette Roque1
1Department of Electronics and Computer Engineering, De La Salle University, 2401 Taft Avenue, Malate, Manila 1004, Philippines.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究开发了菲律宾的洪水预测系统,使用神经模糊的LSTM网络和遗传算法,达到92.91%的准确性. 该系统提高了社区对洪水的认识和准备,旨在减少损害和挽救生命.
科学领域:
- 环境科学 环境科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 洪水在菲律宾构成重大风险,造成财产损失和生命损失.
- 实时监测洪水状况对于提高社区意识和准备工作至关重要.
研究的目的:
- 为菲律宾开发和评估一个安全的洪水预测系统.
- 提高洪水意识,减少洪水事件的影响.
主要方法:
- 开发了一个结合模糊逻辑,长短期记忆 (LSTM) 神经网络和遗传算法的洪水预测模型.
- 使用Infineon安全模块建立了一个安全的数据流,用于将数据传输到AWS物联网核心,时间流和Grafana.
- 使用不同配置的LSTM隐藏层,时代和学习率来比较模型性能.
主要成果:
- 带有遗传算法的神经模糊LSTM网络实现了92.91%的洪水预测准确度.
- 最优的模型配置涉及一个ADAM解决器,每3小时预测洪水,具体的学习率和LSTM隐藏层参数.
- 这种方法与基线ADAM解决方案预测模型相比,显示出更好的准确性.
结论:
- 开发的洪水预测系统显著提高了预测准确度.
- 集成先进的算法和安全的数据传输,为洪水管理提供了可靠的工具.
- 该系统有潜力提高洪水意识和准备,从而减轻洪水相关的损害和挽救生命.
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