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Prediction and modeling of water quality using deep neural networks

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Abstract

Water pollution is one of the most challenging environmental issues. A powerful tool for measuring the suitability of water for drinking is required. The Water Quality Index (WQI) is a widely used parameter for the assessment of water quality through mathematical formulas. In this paper, a Deep Neural Network (DNN) model is developed to forecast WQI based on parameters selected for the dry and wet seasons throughout the year. Statistical modeling and unsupervised machine learning techniques are used. These modelings include the Principal Component Analysis/Factor Analysis (PCA/FA) which is used to interpret seasonal changes and the sources of springs under study. The other modeling technique utilized in this study is the Hierarchical Cluster Analysis (HCA). The results of this study reveal that the developed DNN model has achieved a high accuracy of ***. The goodness of fit of the developed model using R-Squared (R2) is 0.98 which is deemed high. The Mean Square Error metric is close to zero. Furthermore, the PCA/FA revealed five major parameters that impact water quality which together account for 92% of the total variance of water quality in summer and 96% in winter. Moreover, results show that the average of the WQI for all springs is of poor water quality at 46.75% during the dry season and medium water quality at 55.5% during the wet season.

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Some of the data used in this research is publicly available as referenced within this paper. Newly generated data are not public as it is still under study by the authors.

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Authors and Affiliations

  1. Boone Pickens School of Geology, Oklahoma State University, Oklahoma, USA

    Marwa El-Shebli

  2. Computer Science Department, Isra University, Amman, Jordan

    Yousef Sharrab

  3. Software Engineering Department, Isra University, Amman, Jordan

    Dimah Al-Fraihat

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  1. Marwa El-Shebli
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  2. Yousef Sharrab
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Contributions

ME-S is the main author, she provided the data, initialize the writing, and she has implemented the experiments and collaborated to finish the writing. Dr. YS built the model, experiment design, and implementation, and he has finalized the writing. Dr. DA helped in the writing and collaborated to finish the writing.

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Correspondence to Marwa El-Shebli.

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El-Shebli, M., Sharrab, Y. & Al-Fraihat, D. Prediction and modeling of water quality using deep neural networks. Environ Dev Sustain 26, 11397–11430 (2024). https://doi.org/10.1007/s10668-023-03335-5

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