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RESEARCH ARTICLE
Contents Vol 75(9)

An adaptive weighted-average Kriging method applied to monitoring of freshwater ecosystems

Qilu Liu https://orcid.org/0009-0003-1469-3063 A # , Jingfang Shen A # and Yaohui Li https://orcid.org/0009-0001-0231-7437 A B *
+ Author Affiliations
- Author Affiliations

A College of Informatics, Huazhong Agricultural University, Wuhan, 430070, PR China.

B College of Mechanical and Electrical Engineering, Xuchang University, Xuchang, 461000, Henan, PR China.

* Correspondence to: lyh@xcu.edu.cn

Handling Editor: Yong Xiao

Marine and Freshwater Research 75, MF24003 https://doi.org/10.1071/MF24003
Submitted: 16 January 2024  Accepted: 15 May 2024  Published: 20 June 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing

Abstract

Context

The prediction of freshwater quality is important for detecting pollution risks and assessing changes in freshwater ecosystems. As a high-precision interpolation method, Kriging was able to predict freshwater quality by using previously monitored data. However, how to select the key parameters, regression functions and correlation functions of Kriging method in the process of improving prediction accuracy is still a bottleneck.

Aims

This study aims to propose an adaptive weighted-average Kriging (AWAK) method to further enhance the accuracy of freshwater-quality predictions.

Methods

The AWAK method consists of four main steps. First, the key parameters influencing pollution indicators are selected by FPS method. Subsequently, six different Kriging candidate models are constructed by using regression and correlation functions with different characteristics. Then, an enhanced-likelihood function is used to determine the weights of the six Kriging candidate models. Finally, AWAK is built by weighted sum of these six Kriging models.

Key results

The AWAK outperformed traditional Kriging in predicting pH and dissolved oxygen, significantly reducing prediction errors.

Conclusions

By employing the AWAK method, this study successfully improved the accuracy of freshwater-quality predictions.

Implications

The introduction of the AWAK provides an effective approach in the field of freshwater ecology.

Keywords: adaptive weighting, correlation function, enhanced likelihood function, freshwater ecosystems, freshwater-quality prediction, Kriging, regression function, surrogate model.

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