使用混合机器学习,遥感和GIS解决洪水易感的空间推断问题.
Huu Duy Nguyen1, Quoc-Huy Nguyen2, Quang-Thanh Bui2
1Faculty of Geography, VNU University of Science, Vietnam National University, Hanoi, Vietnam. nguyenhuuduy@hus.edu.vn.
Environmental science and pollution research international
|February 13, 2024
概括
本研究介绍了先进的机器学习 (ML) 模型,包括具有各种优化算法的深度神经网络 (DNN),以有效地应对洪水易感性建模挑战. 这些模型成功地解决了外推问题,提高了新地区洪水预测的准确性.
科学领域:
- 环境科学 环境科学
- 地理空间分析的研究.
- 人工智能的人工智能
背景情况:
- 洪水是一个主要的自然灾害,对人类生活和经济的影响越来越大.
- 有效的水资源管理需要改进洪水易感性建模.
- 将洪水模型推广到新的地区仍然是一个重大挑战.
研究的目的:
- 开发和评估机器学习 (ML) 模型,以解决洪水易感性建模中的外推问题.
- 为了比较与各种优化算法集成的深度神经网络 (DNN) 模型的性能.
主要方法:
- 利用深度神经网络 (DNN) 与六个优化算法相结合:地球优化算法 (EOA),野兽群优化 (WHO),基于生物地理的优化 (BBO),青鸟优化器 (SBO),虫优化算法 (GOA) 和粒子群优化 (PSO).
- 将模型应用于昆南省的洪水易感性测绘,并测试了对恩河安省的推断.
- 使用根平均平方误差 (RMSE),接收器操作特征 (ROC),ROC曲线下的面积 (AUC) 和精度 (ACC) 评估模型性能.
主要成果:
- 所有开发的模型都在洪水易感性测绘中表现出高性能,AUC值大于0.9.
- DNN-BBO模型实现了最高的AUC (0.99),紧随其后的是DNN-WHO (0.99) 和DNN-SBO (0.98).
- 这些模型成功地解决了外推问题,证明了它们在新地理区域评估洪水易感性的能力.
结论:
- 新的ML模型有效地解决了洪水易感性建模中的外推问题.
- 这些模型为面临洪水风险的沿海地区的城市规划者和决策者提供了宝贵的参考资料.
- 开发的方法可以适应全球评估洪水易感性,增强防灾准备.
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