Department of Infrastructure Engineering, University of Melbourne, Parkville, VIC, 3010, Australia.
School of Mathematics Physics and Computing, University of Southern Queensland, Springfield, QLD, 4300, Australia.
Environ Sci Pollut Res Int. 2023 Jan;30(3):7851-7873. doi: 10.1007/s11356-022-22601-z. Epub 2022 Sep 1.
Dissolved oxygen (DO) forecasting is essential for aquatic managers responsible for maintaining ecosystem health and the management of water bodies affected by water quality parameters. This paper aims to forecast dissolved oxygen (DO) concentration using a multivariate adaptive regression spline (MARS) hybrid model coupled with maximum overlap discrete wavelet transformation (MODWT) as a feature decomposition approach for Surma River water using a set of water quality hydro-meteorological variables. The proposed hybrid model is compared with numerous machine learning methods, namely Bayesian ridge regression (BNR), k-nearest neighbourhood (KNN), kernel ridge regression (KRR), random forest (RF), and support vector regression (SVR). The investigational results show that the proposed model of MODWT-MARS has a better prediction than the comparing benchmark models and individual standalone counter parts. The result shows that the hybrid algorithms (i.e. MODWT-MARS) outperformed the other models (r = 0.981, WI = 0.990, RMAE = 2.47%, and MAE = 0.089). This hybrid method may serve to forecast water quality variables with fewer predictor variables.
溶解氧（DO）预测对于负责维护生态系统健康和管理受水质参数影响的水体的水生管理人员至关重要。本文旨在使用多元自适应回归样条（MARS）混合模型结合最大重叠离散小波变换（MODWT）作为特征分解方法，使用一组水质水力学气象变量来预测苏里马河水中的溶解氧（DO）浓度。将所提出的混合模型与许多机器学习方法进行了比较，例如贝叶斯岭回归（BNR）、k-最近邻（KNN）、核岭回归（KRR）、随机森林（RF）和支持向量回归（SVR）。研究结果表明，与比较基准模型和单个独立的同类模型相比，提出的 MODWT-MARS 模型具有更好的预测能力。结果表明，混合算法（即 MODWT-MARS）优于其他模型（r=0.981，WI=0.990，RMAE=2.47%，MAE=0.089）。该混合方法可以用于预测具有较少预测变量的水质变量。
