Research on a multiparameter water quality prediction method based on a hybrid model

被引：16

作者：

Zheng, Zhiqiang ^{[1
,2
]}

Ding, Hao ^{[1
]}

Weng, Zhi ^{[1
,2
]}

Wang, Lixin ^{[2
,3
]}

机构：

[1] Inner Mongolia Univ, Coll Elect Informat Engn, Hohhot 010021, Peoples R China

[2] Minist Educ China, Collaborat Innovat Ctr Grassland Ecol Secur, Hohhot 010021, Peoples R China

[3] Inner Mongolia Univ, Sch Ecol & Environm, Hohhot 010021, Peoples R China

来源：

ECOLOGICAL INFORMATICS | 2023年 / 76卷

基金：

中国国家自然科学基金;

关键词：

Water quality prediction; Statistical model; Residual feedback; Neural network; Multivariate data; NETWORK;

D O I：

10.1016/j.ecoinf.2023.102125

中图分类号：

Q14 [生态学（生物生态学）];

学科分类号：

071012 ; 0713 ;

摘要：

Watershed water quality monitoring is of great significance in the protection and management of water envi-ronments. Because existing water quality prediction algorithms cannot achieve high-precision multiparameter analysis and usually require a large amount of data, this paper proposes the VARLST hybrid water quality prediction model. The proposed model combines the traditional statistical vector autoregressive moving average model and the bidirectional long short-term memory neural network to achieve multiparameter water quality data prediction on small data samples, and the model data processing is simple and highly efficient. This model is used to analyze the characteristics of water quality parameters in the Inner Mongolian section of the Yellow River Basin in northern China. The average error and fitting accuracy of the prediction results are 0.0015 and 99.869%, respectively; hence, the model achieves high-accuracy prediction of multiparameter indicators using less data, outperforming single models in terms of feasibility and accuracy.

引用

页数：12