The development of dissolved oxygen forecast model using hybrid machine learning algorithm with hydro-meteorological variables

Article

Ahmed, Abul Abrar Masrur, Jui, S. Janifer Jabin, Chowdhury, Mohammad Aktarul Islam, Ahmed, Oli and Sutradhar, Ambica. 2023. "The development of dissolved oxygen forecast model using hybrid machine learning algorithm with hydro-meteorological variables." Environmental Science and Pollution Research. 30 (3), pp. 7851-7873. https://doi.org/10.1007/s11356-022-22601-z
Article Title

The development of dissolved oxygen forecast model using hybrid machine learning algorithm with hydro-meteorological variables

ERA Journal ID5827
Article CategoryArticle
AuthorsAhmed, Abul Abrar Masrur, Jui, S. Janifer Jabin, Chowdhury, Mohammad Aktarul Islam, Ahmed, Oli and Sutradhar, Ambica
Journal TitleEnvironmental Science and Pollution Research
Journal Citation30 (3), pp. 7851-7873
Number of Pages23
Year2023
PublisherSpringer
Place of PublicationGermany
ISSN0944-1344
1614-7499
Digital Object Identifier (DOI)https://doi.org/10.1007/s11356-022-22601-z
Web Address (URL)https://link.springer.com/article/10.1007/s11356-022-22601-z
AbstractDissolved 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.
KeywordsBangladesh; Dissolved oxygen; Forecasting; Hybrid model; MARS; MODWT; Surma River
ANZSRC Field of Research 20204199. Other environmental sciences
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Byline AffiliationsUniversity of Melbourne
School of Mathematics, Physics and Computing
Shahjalal University of Science and Technology, Bangladesh
Leading University, Bangladesh
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