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用混合元启发和机器学习模型对洪水空间分析的特征选择模型进行比较分析

Javeria Sarwar^1,2, Saud Ahmed Khan¹, Muhammad Azmat³

¹Pakistan Institute of Development Economics, Islamabad, Pakistan.

Environmental science and pollution research international

|April 29, 2024

概括

本研究引入了一种用于液压分析的新型特征选择模型,该模型将粒子集群优化 (PSO) 与K-最近邻居 (KNN) 结合起来. PSO-KNN混合有效地识别最佳数据子集,以改善洪水预测和液压建模.

科学领域:

环境科学环境科学
计算机科学计算机科学
液压工程液压工程液压工程

背景情况:

用于洪水预测的液压分析经常面临复杂数据集的挑战.
现有的特征选择模型对于具有多对线性,异构性和自相关性的数据集是不够的.
液压数据中的非线性特征需要先进的建模技术.

研究的目的:

提出用于液压分析的新型特征选择模型.
评估混合的元启发和机器学习模型,以获得最佳的特征选择.
为了解决当前液压建模方法的局限性.

主要方法:

混合的元启发算法 (粒子群优化,殖民地优化,遗传算法) 与机器学习模型 (支持矢量机器,K-最近邻居).
支持向量机内核 (线性,RBF,西格形,多项式) 和K-最近邻居的K-值的评估.
数据集包括与洪水相关的地形,地理环境和人为诱导的变量.

主要成果:

粒子集群优化 (PSO) 在较少代的功能选择中表现出卓越的性能.
支持矢量机 (SVM) 的辐射基函数 (RBF) 内核显示出最好的准确性,而sigmoid表现不佳.
PSO-K-近邻 (KNN) 混合模型的性能优于其他组合,在液压分析中实现了最佳特征选择精度.

结论:

拟议的PSO-KNN模型是有效的特征选择在液压分析.
使用PSO-KNN可以获得最佳的数据子集,以改进液压建模.
这种方法提高了洪水预测模型的准确性和效率.

关键词:

功能选择功能选择洪水洪水洪水洪水混合元启发算法混合元启发算法机器学习模型的机器学习模型

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