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Analysis and forecasting of rivers pH level using deep learning

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Abstract

The pH level of oceans has been largely monitored and studied to make sure that aquatic ecosystems are thriving. However, the pH level of other large bodies of waters, such as rivers, has largely been glanced over. Many rivers contain very sensitive underwater ecosystems, and as a result even small pH changes can largely impact the relative biodiversity. With the addition of increased carbon emissions and pollution, large bodies of water are absorbing more carbon and consequently the pH levels of rivers can rapidly change. This paper studies different deep learning approaches to analyze and forecast pH levels, which include a long short-term memory (LSTM), gated recurrent unit (GRU), recurrent neural network (RNN), and a Temporal Fusion Transformer (TFT) model to determine which algorithm provides the best pH predictive forecast. We demonstrate that the TFT outperforms other deep learning methods through various metrics. In addition, we clarify the importance of temperature as a feature in pH prediction. Lastly, we use the TFT to predict pH anomalies and discover the significance of the predicted data. We found that nine out of the ten predicted data sets have a significant difference compared to the original data.

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References

  1. Weiss, L.C., Pötter, L., Steiger, A., Kruppert, S., Frost, U., Tollrian, R.: Rising pCO2 in freshwater ecosystems has the potential to negatively affect predator-induced defenses in Daphnia. Curr. Biol. 28, 327–332 (2018)

    Article  Google Scholar 

  2. Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.-A.: Deep learning for time series classification: a review. Data Min. Knowl. Disc. 33(4), 917–963 (2019)

    Article  MathSciNet  Google Scholar 

  3. Cremer, C.Z.: Deep limitations? examining expert disagreement over deep learning. Prog. Artif. Intell. 1–16 (2021, in press)

  4. Lim, B., Arik, S.O., Loeff, N., Pfister, T.: Temporal fusion transformers for interpretable multi-horizon time series forecasting. Int. J. Forecast. (2021)

  5. Kang, G., Gao, J.Z., Xie, G.: Data-driven water quality analysis and prediction: a survey. In: IEEE International Conference on Big Data Computing Service and Applications, pp. 224–232 (2017)

  6. Wootton, J.T., Pfister, C.A., Forester, J.D.: Dynamic patterns and ecological impacts of declining ocean pH in a high-resolution multi-year dataset. Proc. Natl. Acad. Sci. 105(48), 18848–18853 (2008)

    Article  Google Scholar 

  7. Zare Abyaneh, H.: Evaluation of multivariate linear regression and artificial neural networks in prediction of water quality parameters. J. Environ. Health Sci. Eng. 12(1), 40 (2014)

    Article  Google Scholar 

  8. Palani, S., Liong, S.-Y., Tkalich, P.: An ANN application for water quality forecasting. Mar. Pollut. Bull. 56(9), 1586–1597 (2008)

    Article  Google Scholar 

  9. Yingyi, C., Songk, L., Liu, Y., Yang, L., Li., D.: A review of the artificial neural network models for water quality prediction. Appl. Sci. 10 (2020)

  10. Tiwari, S., Babbar, R., Kaur, G.: Performance evaluation of two ANFIS models for predicting water quality index of River Satluj (India). Adv. Civ. Eng. 2018, 8971079 (2018)

    Google Scholar 

  11. Najah Ahmed, A., et al.: Machine learning methods for better water quality prediction. J. Hydrol. 578, 124084 (2019)

    Article  Google Scholar 

  12. Khadr, M., Elshemy, M.: Data-driven modeling for water quality prediction case study: the drains system associated with Manzala Lake, Egypt. Ain Shams Eng. J. 8(4), 549–557 (2017)

    Article  Google Scholar 

  13. Azad, A., et al.: Prediction of water quality parameters using ANFIS optimized by intelligence algorithms (case study: Gorganrood River). KSCE J. Civ. Eng. 22(7), 2206–2213 (2018)

    Article  Google Scholar 

  14. Barzegar, R., Adamowski, J., Moghaddam, A.A.: Application of wavelet-artificial intelligence hybrid models for water quality prediction: a case study in Aji-Chay River, Iran. Stoch. Environ. Res. Risk Assess. 30(7), 1797–1819 (2016)

    Article  Google Scholar 

  15. Khan, Y., Chai, S.: Ensemble of ANN and ANFIS for water quality prediction and analysis-a data driven approach. J. Telecommun. Electron. Comput. Eng. 9(2–9), 117–122 (2017)

  16. Hu, Z., et al.: A water quality prediction method based on the deep LSTM network considering correlation in smart mariculture. Sensors 19(6) (2019)

  17. Aldhyani, T.H.H., Al-Yaari, M., Alkahtani, H., Maashi, M.: Water quality prediction using artificial intelligence algorithms. Appl. Bionics Biomech. 2020, 6659314 (2020)

    Article  Google Scholar 

  18. Yang, Y., et al.: A study on water quality prediction by a hybrid CNN-LSTM model with attention mechanism. Environ. Sci. Pollut. Res. (2021)

  19. Barzegar, R., Aalami, M.T., Adamowski, J.: Short-term water quality variable prediction using a hybrid CNN-LSTM deep learning model. Stoch. Environ. Res. Risk Assess. 34(2), 415–433 (2020)

    Article  Google Scholar 

  20. Wang, Y., Zhou, J., Chen, K., Wang, Y., Liu, L.: Water quality prediction method based on LSTM neural network. In: International Conference on Intelligent Systems and Knowledge Engineering, pp. 1–5 (2017)

  21. Ye, Q., Yang, X., Chen, C., Wang, J.: River water quality parameters prediction method based on LSTM-RNN model. In: Chinese Control And Decision Conference, pp. 3024–3028 (2019)

  22. Baek, S.-S., Pyo, J., Chun, J.A.: Prediction of water level and water quality using a CNN-LSTM combined deep learning approach. Water 12(12) (2020)

  23. Li, Z., et al.: Water quality prediction model combining sparse auto-encoder and LSTM network. IFAC-PapersOnLine 51(17), 831–836 (2018)

    Article  Google Scholar 

  24. Cao, X., Wang, J., Duan, Q., et al.: Prediction model of key water quality factors in pond culture based on wavelet denoising and CNN-LSTM. Int. Agric. Eng. J. 28(4) (2019)

  25. Langelier, W.: Effect of temperature on the pH of natural waters. J. Am. Water Works Assoc. 38(2), 179–185 (1946)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Virginia Commonwealth University, 401 W. Main St, Richmond, 23284, VA, USA

    Abhay Srivastava & Alberto Cano

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  1. Abhay Srivastava
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  2. Alberto Cano
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Correspondence to Alberto Cano.

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Srivastava, A., Cano, A. Analysis and forecasting of rivers pH level using deep learning. Prog Artif Intell 11, 181–191 (2022). https://doi.org/10.1007/s13748-021-00270-2

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