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
Public Notes

Files associated with this item cannot be displayed due to copyright restrictions.

Byline AffiliationsUniversity of Melbourne
School of Mathematics, Physics and Computing
Academic Registrar's Office
Shahjalal University of Science and Technology, Bangladesh
Leading University, Bangladesh
Permalink -

https://research.usq.edu.au/item/z2527/the-development-of-dissolved-oxygen-forecast-model-using-hybrid-machine-learning-algorithm-with-hydro-meteorological-variables

Log in to edit
  • 32
    total views
  • 0
    total downloads
  • 1
    views this month
  • 0
    downloads this month

Export as

Related outputs

Real-time prediction of the week-ahead flood index using hybrid deep learning algorithms with synoptic climate mode indices
Ahmed, A.A. Masrur, Akther, Shahida, Nguyen-Huy, Thong, Raj, Nawin, Jui (Student), S Janifer and S.Z., Farzana. 2024. "Real-time prediction of the week-ahead flood index using hybrid deep learning algorithms with synoptic climate mode indices." Journal of Hydro-Environment Research. 57, pp. 12-26. https://doi.org/10.1016/j.jher.2024.09.001
An advanced deep learning predictive model for air quality index forecasting with remote satellite-derived hydro-climatological variables
Ahmed, Abul Abrar Masrur, Jui, S. Janifer Jabin, Sharma, Ekta, Ahmed, Mohammad Hafez, Raj, Nawin and Bose, Aditi. 2024. "An advanced deep learning predictive model for air quality index forecasting with remote satellite-derived hydro-climatological variables." Science of the Total Environment. 906. https://doi.org/10.1016/j.scitotenv.2023.167234
Hybrid Deep Learning Model for Wave Height Prediction in Australia's Wave Energy Region
Ahmed, Abul Abrar Masrur, Jui, S Janifer Jabin, AL-Musaylh, Mohanad S., Raj, Nawin, Saha, Reepa, Deo, Ravinesh C and Saha, Sanjoy Kumar. 2024. "Hybrid Deep Learning Model for Wave Height Prediction in Australia's Wave Energy Region." Applied Soft Computing. 150. https://doi.org/10.1016/j.asoc.2023.111003
Application of Entropy for Automated Detection of Neurological Disorders With Electroencephalogram Signals: A Review of the Last Decade (2012-2022)
Jui, S. Janifer Jabin, Deo, Ravinesh C. Deo, Barua, Prabal Datta, Devi, Aruna, Soar, Jeffrey and Acharya, U. Rajendra. 2023. "Application of Entropy for Automated Detection of Neurological Disorders With Electroencephalogram Signals: A Review of the Last Decade (2012-2022)." IEEE Access. 11, pp. 71905-71924. https://doi.org/10.1109/ACCESS.2023.3294473
Cloud cover bias correction in numerical weather models for solar energy monitoring and forecasting systems with kernel ridge regression
Deo, Ravinesh C., Ahmed, A.A. Masrur, Casillas-Perez, David, Pourmousavi, S. Ali, Segal, Gary, Yu, Yanshan and Salcedo-sanz, Sancho. 2023. "Cloud cover bias correction in numerical weather models for solar energy monitoring and forecasting systems with kernel ridge regression." Renewable Energy. 203, pp. 113-130. https://doi.org/10.1016/j.renene.2022.12.048
Introductory Engineering Mathematics Students’ Weighted Score Predictions Utilising a Novel Multivariate Adaptive Regression Spline Model
Ahmed, Abul Abrar Masrur, Deo, Ravinesh C., Ghimire, Sujan, Downs, Nathan J., Devi, Aruna, Barua, Prabal D. and Yaseen, Zaher M.. 2022. "Introductory Engineering Mathematics Students’ Weighted Score Predictions Utilising a Novel Multivariate Adaptive Regression Spline Model." Sustainability. 14 (17), pp. 1-27. https://doi.org/10.3390/su141711070
Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors
Ahmed, A. A. Masrur, Sharma, Ekta, Jui, S. Janifer Jabin, Deo, Ravinesh C., Nguyen-Huy, Thong and Ali, Mumtaz. 2022. "Kernel Ridge Regression Hybrid Method for Wheat Yield Prediction with Satellite-Derived Predictors." Remote Sensing. 14 (5), pp. 1-24. https://doi.org/10.3390/rs14051136
Assessment and Prediction of Sea Level Trend in the South Pacific Region
Raj, Nawin, Gharineiat, Zahra, Ahmed, Abul Abrar Masrur and Stepanyants, Yury. 2022. "Assessment and Prediction of Sea Level Trend in the South Pacific Region." Remote Sensing. 14 (4), pp. 1-25. https://doi.org/10.3390/rs14040986
New double decomposition deep learning methods for river water level forecasting
Ahmed, A. A. Masrur, Deo, Ravinesh C., Ghahramani, Afshin, Feng, Qi, Raj, Nawin, Yin, Zhenliang and Yang, Linshan. 2022. "New double decomposition deep learning methods for river water level forecasting." Science of the Total Environment. 831, pp. 1-21. https://doi.org/10.1016/j.scitotenv.2022.154722
Spatiotemporal Hybrid Random Forest Model for Tea Yield Prediction Using Satellite-Derived Variables
Jui, S. Janifer Jabin, Ahmed, A. A. Masrur, Bose, Aditi, Raj, Nawin, Sharma, Ekta, Soar, Jeffrey and Chowdhury, Md Wasique Islam. 2022. "Spatiotemporal Hybrid Random Forest Model for Tea Yield Prediction Using Satellite-Derived Variables." Remote Sensing. 14 (3), pp. 1-18. https://doi.org/10.3390/rs14030805
Optimization algorithms as training approach with hybrid deep learning methods to develop an ultraviolet index forecasting model
Ahmed, A. A. Masrur, Ahmed, Mohammad Hafez, Saha, Sanjoy Kanti, Ahmed, Oli and Sutradhar, Ambica. 2022. "Optimization algorithms as training approach with hybrid deep learning methods to develop an ultraviolet index forecasting model." Stochastic Environmental Research and Risk Assessment. 36 (10), pp. 3011-3039. https://doi.org/10.1007/s00477-022-02177-3
Development of Deep Learning Hybrid Models for Hydrological Predictions
Ahmed, Abul Abrar Masrur. 2022. Development of Deep Learning Hybrid Models for Hydrological Predictions. PhD by Publication Doctor of Philosophy. University of Southern Queensland. https://doi.org/10.26192/q7q5z
Hybrid deep learning method for a week-ahead evapotranspiration forecasting
Ahmed, A. A. Masrur, Deo, Ravinesh C., Feng, Qi, Ghahramani, Afshin, Raj, Nawin, Yin, Zhenliang and Yang, Linshan. 2022. "Hybrid deep learning method for a week-ahead evapotranspiration forecasting." Stochastic Environmental Research and Risk Assessment. 36 (3), pp. 831-849. https://doi.org/10.1007/s00477-021-02078-x
Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data
Ahmed, A. A. Masrur, Deo, Ravinesh C., Raj, Nawin, Ghahramani, Afshin, Feng, Qi, Yin, Zhenliang and Yang, Linshan. 2021. "Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data." Remote Sensing. 13 (4), pp. 1-30. https://doi.org/10.3390/rs13040554
LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios
Ahmed, A. A. Masrur, Deo, Ravinesh C., Ghahramani, Afshin, Raj, Nawin, Feng, Qi, Yin, Zhenliang and Yang, Linshan. 2021. "LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios." Stochastic Environmental Research and Risk Assessment. 35, pp. 1851-1881. https://doi.org/10.1007/s00477-021-01969-3
Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity
Ahmed, A. A. Masrur, Deo, Ravinesh C., Feng, Qi, Ghahramani, Afshin, Raj, Nawin, Yin, Zhenliang and Yang, Linshan. 2021. "Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity." Journal of Hydrology. 599, pp. 1-23. https://doi.org/10.1016/j.jhydrol.2021.126350