water quality prediction research paper

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• Review Article
• Published: 12 March 2024

Deep learning for water quality

• Wei Zhi 1 , 2 ,
• Alison P. Appling   ORCID: orcid.org/0000-0003-3638-8572 3 ,
• Heather E. Golden   ORCID: orcid.org/0000-0001-5501-9444 4 ,
• Joel Podgorski   ORCID: orcid.org/0000-0003-2522-1021 5 &
• Li Li   ORCID: orcid.org/0000-0002-1641-3710 2  

Nature Water volume  2 ,  pages 228–241 ( 2024 ) Cite this article

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Understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in the future. These challenges arise partly due to complex processes that regulate water quality, and arduous and expensive data collection that exacerbate the issue of data scarcity. Traditional process-based and statistical models often fall short in predicting water quality. In this Review, we posit that deep learning represents an underutilized yet promising approach that can unravel intricate structures and relationships in high-dimensional data. We demonstrate that deep learning methods can help address data scarcity by filling temporal and spatial gaps and aid in formulating and testing hypotheses via identifying influential drivers of water quality. This Review highlights the strengths and limitations of deep learning methods relative to traditional approaches, and underscores its potential as an emerging and indispensable approach in overcoming challenges and discovering new knowledge in water-quality sciences.

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Data availability.

Streamflow data (Fig. 1a ) from the Global Streamflow Indices and Metadata Archive (GSIM) were compiled from repositories at https://doi.org/10.1594/PANGAEA.887477 and https://doi.org/10.1594/PANGAEA.887470 . Water-quality data (Fig. 1b ) from the Global River Water Quality Archive (GRQA) were downloaded from https://doi.org/10.5281/zenodo.7056647 .

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Acknowledgements

W.Z. was supported by the National Natural Science Foundation of China (52121006) and by the Barry and Shirley Isett Professorship (to L.L.) at Penn State University. L.L. was supported by the US National Science Foundation via the Critical Zone Collaborative Network (EAR-2012123 and EAR-2012669), Frontier Research in Earth Sciences (EAR-2121621), Signals in Soils (EAR-2034214), and US Department of Energy Environmental System Science (DE-SC0020146). J.P. was supported by Swiss Agency for Development and Cooperation (SDC) (WABES project, 7F-09963.02.01). This paper has been reviewed in accordance with the US Environmental Protection Agency’s peer and administrative review policies and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement or recommendation for use by the US Government. Statements in this publication reflect the authors’ professional views and opinions and should not be construed to represent any determination or policy of the US Environmental Protection Agency.

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Machine learning algorithms for efficient water quality prediction

• Original Article
• Published: 26 August 2021
• Volume 8 , pages 2793–2801, ( 2022 )

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• Mourade Azrour   ORCID: orcid.org/0000-0003-1575-8140 1 ,
• Jamal Mabrouki 2 ,
• Ghizlane Fattah 3 ,
• Azedine Guezzaz 4 &
• Faissal Aziz 5  

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Water is an essential resource for human existence. In fact, more than 60% of the human body is made up of water. Our bodies consume water in every cell, in the different organisms and in the tissues. Hence, water allows stabilization of the body temperature and guarantees the normal functioning of the other bodily activities. Nevertheless, in recent years, water pollution has become a serious problem affecting water quality. Therefore, to design a model that predicts water quality is nowadays very important to control water pollution, as well as to alert users in case of poor quality detection. Motivated by these reasons, in this study, we take the advantages of machine learning algorithms to develop a model that is capable of predicting the water quality index and then the water quality class. The method we propose is based on four water parameters: temperature, pH, turbidity and coliforms. The use of the multiple regression algorithms has proven to be important and effective in predicting the water quality index. In addition, the adoption of the artificial neural network provides the most highly efficient way to classify the water quality.

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Mourade Azrour

Laboratory of Spectroscopy, Molecular Modeling, Materials, Nanomaterial, Water and Environment, CERNE2D, Faculty of Science, Mohammed V University in Rabat, Rabat, Morocco

Jamal Mabrouki

Civil Hydraulic and Environmental Engineering Laboratory, Water Treatment and Reuse Structure, Mohammadia School of Engineers, Mohammed V University in Rabat, Avenue Ibn Sina B.P 765, 10090, Agdal Rabat, Morocco

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Department of Computer Science and Mathematics, High School of Technology, Cadi Ayyad University, 44000, Essaouira, Morocco

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Laboratory of Water, Biodiversity and Climate Change, Semlalia Faculty of Sciences, University Cadi Ayyad, Marrakech, Morocco

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Azrour, M., Mabrouki, J., Fattah, G. et al. Machine learning algorithms for efficient water quality prediction. Model. Earth Syst. Environ. 8 , 2793–2801 (2022). https://doi.org/10.1007/s40808-021-01266-6

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Received : 29 June 2021

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Published : 26 August 2021

Issue Date : June 2022

DOI : https://doi.org/10.1007/s40808-021-01266-6

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A review of the artificial neural network models for water quality prediction.

1. Introduction

• First, we identified ANN-related papers in influential water-related and environmental-related journals to ensure that high-quality papers are included in the review. These papers are mainly from journals whose subjects are environmental science and ecology, water resources, engineering and application.
• Thereafter, a keyword search of the ISI Web of Science was then conducted for the period 2008–2019 using the keywords; water quality, river, lake, reservoir, WWTP, groundwater, pond, prediction, and forecasting, accompanied by the names of ANN methods (one or more), such as neural network, MLP, RBFNN, GRNN, RNN, to name but a few.
• Then, through the search process from 1 to 2, 151 articles in English relevant to our focus were selected. The basic information of the papers, including authors (year), locations, water quality variables, meteorological factors, other factors, output strategy, data size, time step, data dividing, methods, and prediction lengths are provided in Appendix A .

3. Three Basic Model Structures in Water Quality Prediction

3.1. feedforward architectures, 3.2. recurrent architectures, 3.3. hybrid architectures, 3.4. emerging methods, 4. artificial neural networks models for water quality prediction, 5.1. data collection, 5.2. output strategy, 5.3. input selection, 5.4. data dividing, 5.5. data preprocessing, 5.6. model structure determination, 5.7. model training, 6. discussion, 6.1. data are the foundation, 6.2. data processing is key, 6.3. model is the core, author contributions, acknowledgments, conflicts of interest.

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Chen, Y.; Song, L.; Liu, Y.; Yang, L.; Li, D. A Review of the Artificial Neural Network Models for Water Quality Prediction. Appl. Sci. 2020 , 10 , 5776. https://doi.org/10.3390/app10175776

Chen Y, Song L, Liu Y, Yang L, Li D. A Review of the Artificial Neural Network Models for Water Quality Prediction. Applied Sciences . 2020; 10(17):5776. https://doi.org/10.3390/app10175776

Chen, Yingyi, Lihua Song, Yeqi Liu, Ling Yang, and Daoliang Li. 2020. "A Review of the Artificial Neural Network Models for Water Quality Prediction" Applied Sciences 10, no. 17: 5776. https://doi.org/10.3390/app10175776

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On the Search of the Optimum Method for Water Quality Prediction Using Machine Learning Techniques Combined with Advanced Sampling Techniques

20 Pages Posted: 3 Sep 2024

Rahmi Fadhilah

Institut Teknologi Sepuluh Nopember

Heri Kuswanto

affiliation not provided to SSRN

Dedy Dwi Prastyo

Handling Imbalanced datasets is a significant challenge in water quality assessment. This study evaluates the performance of three machine learning models Naïve Bayes (NB), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) on imbalanced water quality datasets. Various sampling strategies, including Random Undersampling (RUS), Rapidly Converging Gibbs Sampler (RACOG) and a combined RACOG-RUS approach, were employed to enhance model performance. The analysis shows notable variation in model accuracy and F1 scores depending on the sampling method and wheter feature selection was applied. XGBoost with RACOG achieved the highest performance without feature selection (accuracy: 0,958), while Naïve Bayes with RUS performed exceptionally well (accuracy: 0,986; F1 Score: 0,979). With feature selection, XGBoost with RACOG outperformed other models, reaching an F1 score of 0,982 and accuracy (0,606). These findings highlight the importance of advanced sampling techniques and feature selection in enhancing machine learning models for water quality classification. The method used:Apply advanced sampling techniques to handle imbalanced datasets effectively;Evaluated model performance with and without feature selection to identify the best approach;Enhance classification accuracy for better water quality assessment.

Keywords: Water Quality Classification, Imbalanced Data Handling, Random Forest, Naïve Bayes, XGBoost, Random Under Sampling (RUS), Rapidly Converging Gibbs Sampler (RACOG), RACOG-RUS

Suggested Citation: Suggested Citation

Institut Teknologi Sepuluh Nopember ( email )

Surabaya, 60111 Indonesia

Heri Kuswanto (Contact Author)

Affiliation not provided to ssrn ( email ).

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