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>人工智能模型的水质预测:中国河流域的案例,中国

Jing Chen^1,2, Haiyang Li³, Manirankunda Felix¹

¹School of Electrical and Information Engineering, Anhui University of Science and Technology, No. 168, Taifeng Road, Huainan, 232001, China.

Environmental science and pollution research international

|January 25, 2024

概括

本研究介绍了GRA-LMA模型用于准确的水质预测,通过将长短期记忆 (LSTM) 与多头注意力和先进的数据处理技术相结合,显著减少错误.

科学领域:

环境科学环境科学
数据科学数据科学数据科学
人工智能的人工智能

背景情况:

水质预测对于资源管理至关重要,但与时间序列数据,非线性和数据质量问题面临挑战.
像LSTM这样的现有模型经常忽视并行计算,并与复杂的数据相关性和缺失/异常值作斗争.

研究的目的:

开发一个先进的水质预测模型 (GRA-LMA),解决现有方法的局限性.
提高水质指数预测的准确性和可靠性.

主要方法:

一个新的LSTM多头注意力 (LMA) 神经网络被开发用于时间序列处理和特征提取.
该GRA-LMA模型整合了线性插值用于数据归算和灰色关系分析用于特征选择.
该模型使用来自河流域的水质数据进行验证,并将其性能与传统模型 (BP,RNN,LSTM,GRU) 进行比较.

主要成果:

线性插值有效地修复了缺失和异常数据,而不会扭曲模式.
灰色关系分析减少了输入数据的数量,同时保持了基本的相关性.
与其他模型相比,GRA-LMA模型表现出卓越的预测性能,显著降低了多个水质指数的平均平方误差.

结论:

GRA-LMA模型为水质预测提供了一个强大的解决方案,有效地处理复杂的数据挑战.
将LMA与数据预处理技术集成,提高了智能水资源管理的预测准确性和可靠性.

关键词:

灰色关系分析灰色关系分析华河河流盆地华河河流盆地线性互波线性互波线性互波线性长期短期记忆长期短期记忆多头的注意力注意力.预测水质水质预测

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