多策略改进雪消融优化器:以核极值学习机优化洪水预报为例

IF 13.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Lele Cui, Gang Hu, Yaolin Zhu
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引用次数: 0

摘要

Kernel Extreme Learning Machine (KELM)具有自动提取数据特征,从历史数据中学习和处理非线性问题的优点,对于原因复杂、突发性的洪水预测问题,可以帮助获得更好的预测结果。传统的洪水灾害预测通常只考虑一个影响因素,而没有考虑影响洪水发生的复杂因素。本文提出了一种基于20个影响因子的洪水发生概率预测方法。首先,为了更好地利用KELM的性能,提出了一种改进的融雪优化算法(MESAO),将基于水平的选择压力机制、协方差矩阵学习策略、基于历史位置的边界调整策略和随机质心反向学习策略引入融雪优化(SAO)中,用于后续实验。其次,利用MESAO对KELM模型的正则化系数C和核函数参数S进行超参数优化。最后,构建了MESAO-KELM在洪水预报问题中的多特征输入输出模型。在超参数优化方面,该方法的数值实验结果优于其他10种智能算法和5种回归预测模型的预测结果。评价指标结果表明,MESAO优化KELM的适应性最佳,预报精度和稳定性优于其他预报模型。该方法克服了传统的基于单一影响因素的预测模型的局限性,能够基于复杂多变的因素对洪水发生概率进行预测。可以说MESAO-KELM具有较强的泛化能力。准确的洪水预报可以提供早期预警和提前采取措施,以保护和减少洪水对人类和社会发展的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-strategy improved snow ablation optimizer: a case study of optimization of kernel extreme learning machine for flood prediction

The Kernel Extreme Learning Machine (KELM) has the advantage of automatically extracting data features, learning and processing nonlinear problems from historical data, which can help achieve better prediction results for flood prediction problems with complex and sudden causes. Traditional flood disaster prediction usually only considers one influencing factor without considering the complex factors that affect flood occurrence. This article develops a new method for predicting the probability of flood occurrence based on 20 influencing factors. Firstly, in order to better utilize KELM performance, an improved snow ablation optimization algorithm (MESAO) was proposed for subsequent experiments by introducing a level based selection pressure mechanism, covariance matrix learning strategy, historical position based boundary adjustment strategy, and random centroid reverse learning strategy into snow ablation optimization (SAO). Secondly, MESAO is used to perform hyperparameter optimization on the regularization coefficient C and kernel function parameter S of the KELM model. Finally, the construction of a multi feature input–output model for the application of MESAO-KELM in flood prediction problems was completed. In terms of hyperparameter optimization, the numerical experimental results of this method were superior to the prediction results of 10 other intelligent algorithms and 5 regression prediction models. According to the evaluation index results, the best adaptability of MESAO optimized KELM and higher prediction accuracy and stability compared to other prediction models were demonstrated. This method overcomes the limitations of traditional prediction models based on a single influencing factor and can predict the probability of flood occurrence based on complex and variable factors. It can be said that MESAO-KELM has strong generalization ability. Accurate flood prediction can provide early warning and take measures in advance to protect and reduce the impact of floods on human and social development.

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来源期刊
Artificial Intelligence Review
Artificial Intelligence Review 工程技术-计算机:人工智能
CiteScore
22.00
自引率
3.30%
发文量
194
审稿时长
5.3 months
期刊介绍: Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.
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