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

• Published in: Environmental science and pollution research international Vol. 30; no. 3; pp. 7851 - 7873
• Main Authors: Ahmed, Abul Abrar Masrur, Jui, S. Janifer Jabin, Chowdhury, Mohammad Aktarul Islam, Ahmed, Oli, Sutradha, Ambica
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2023; Springer Nature B.V
• Subjects: Algorithms; Aquatic ecosystems; Aquatic Pollution; Artificial intelligence; Atmospheric Protection/Air Quality Control/Air Pollution; Bayes Theorem; Bayesian analysis; Bayesian theory; Decomposition; Discrete Wavelet Transform; Dissolved oxygen; Earth and Environmental Science; Ecosystem; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental Monitoring - methods; Feature decomposition; Feature selection; Forecasting; Hydrology; Hydrometeorology; Kernel functions; Learning algorithms; Machine Learning; Mathematical models; Neural networks; Oxygen; prediction; Regression; regression analysis; Research Article; river water; Rivers; Support vector machines; Time series; Variables; Waste Water Technology; Water Management; Water Pollution Control; Water quality; wavelet; Wavelet transforms; United States > US; Bangladesh; MARS; Hybrid model; Dissolved oxygen; Surma River; Forecasting; MODWT
• ISSN: 0944-1344; 1614-7499; 1614-7499
• DOI: 10.1007/s11356-022-22601-z

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.