计算机与现代化 ›› 2024, Vol. 0 ›› Issue (05): 33-37.doi: 10.3969/j.issn.1006-2475.2024.05.007

• 人工智能 • 上一篇    下一篇

基于AFSPSO-ν-SVM的山洪灾害预测方法研究#br# #br#

  

  1. (1. 西安交通工程学院土木工程学院,陕西 西安 710300; 2. 西安交通工程学院机械与电气工程学院,陕西 西安 710300;
    3. 陕西省外经贸建设集团有限公司,陕西 西安 710003)
  • 出版日期:2024-05-29 发布日期:2024-06-12
  • 作者简介: 作者简介:曹宁(1984—),女,陕西西安人,副教授,硕士,研究方向:人工智能算法,E-mail: 254560472@qq.com; 徐根祺(1984—),男,副教授,硕士,研究方向:地质灾害监测预警,E-mail: 271427071@qq.com; 刘浩(1998—),男,讲师,硕士,研究方向:机器学习和群智能算法,E-mail: 920552210@qq.com。
  • 基金资助:
    陕西省自然科学基础研究计划项目(2023-JC-YB-464); 陕西省教育厅科学研究计划项目(23JP087); 国家自然科学基金资助项目(51578461)
      

Prediction Method of Mountain Flood Disaster Based on AFSPSO-ν-SVM

  1. (1. School of Civil Engineering, Xi’an Traffic Enginering Institute, Xi’an 710300, China; 
    2. School of Mechanical and Electrical Engineering, Xi’an Traffic Enginering Institute, Xi’an 710300, China;
    3. Shaanxi Foreign Economic and Trade Construction Group Co. Ltd., Xi’an 710003, China)
  • Online:2024-05-29 Published:2024-06-12

摘要: 摘要:随着科学技术的发展,人类在山区的工程活动越来越频繁,这加剧了山洪灾害的发生频率。准确及时预测出山洪灾害发生的可能性,对于保证工程安全、降低经济损失、提高人员安全防范能力具有重要意义。将人工智能算法应用于山洪灾害预测成为当下研究者们关注的焦点。为了解决当下山洪诱发因素敏感性差异导致的预测精度不足、小样本数据引起的模型拟合效果欠优以及非线性模型参数不易确定等问题,将主成分分析与ν支持向量机相结合对山洪发生进行预测,通过人工鱼群算法扩大粒子群算法中粒子的搜索范围和速度,并利用改进粒子群算法对支持向量机参数进行寻优,建立AFSPSO-ν-SVM山洪灾害概率预测模型。通过实验对比了本文模型与BL模型、ν-SVM模型、PSO-ν-SVM模型的性能,结果表明,本文模型误差最小且速度最快。本文研究为山洪预报预警领域研究提供了一种新的思路。






关键词: 关键词:人工鱼群算法, 粒子群算法, 支持向量机, 山洪灾害, 预测模型

Abstract: Abstract: With the development of science and technology, human engineering activities in mountainous areas are becoming increasingly frequent, which exacerbating the frequency of flash floods. Accurately and timely predicting the possibility of mountain flood disasters is of great significance for ensuring engineering safety, reducing economic losses, and improving personnel safety prevention capabilities. The application of artificial intelligence algorithms in predicting mountain flood disasters has become the focus of current researchers. In order to solve the problems of insufficient prediction accuracy caused by sensitivity differences in triggering factors of mountain floods, suboptimal model fitting effect caused by small sample data, and difficulty in determining nonlinear model parameters, the principal component analysis and ν support vector machines are combined for predicting flash floods, using artificial fish swarm algorithm to expand the search range and speed of particles in particle swarm algorithm, and using improved particle swarm algorithm to optimize support vector machine parameters, AFSPSO-ν-SVM probability prediction model for mountain flood disasters is established. Through experiments, the proposed model was compared with BL models, ν-SVM model, PSO-ν-SVM model. The results of experiment show that the proposed model has the smallest error and the fastest speed. The paper provides a new approach for research in the field of flash flood forecasting and warning.

Key words: Key words: artificial fish swarm algorithm, particle swarm algorithm, support vector machine, mountain torrent disaster, prediction model

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