DOI: https://doi.org/10.14710/geoplanning.12.1.%25p

Modelling Itasy Lake Water Quality by Long Short Term Memory (LSTM) using Landsat8 Data

*Randrianiaina Jerry Jean Christien Frederick  -  Science Faculty, University of Antananarivo,Madagascar, Madagascar
Rakotonirina Rija Itokiana  -  Laboratory of Matter and Radiation Physics (LPMR), University of Antananarivo, Madagascar
Jean Robertin Rasoloariniaina scopus  -  Institut d’Enseignement Supérieur d’Antsirabe-Vakinankaratra, University of Antananarivo, Madagascar, Madagascar
Fils Lahatra Razafindramisa scopus  -  Laboratory of Matter and Radiation Physics (LPMR), University of Antananarivo, Madagascar, Madagascar

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Abstract

Modelling lake water quality is very important to preserve and protect this resource. Several algorithms can be used to model lake water quality using the in-situ measurements data. This work used The Long Short Term Memory (LSTM) deep learning (DL) architecture to obtain models for modelling and predicting water quality parameters depending on the reflectance of Landsat8 OLI. The obtained results showed the performance of the developed model, LSTM, with Adaptive Moment Estimation (Adam) optimization algorithm that provides an excellent concordance between the collected and simulated water quality parameters. Moreover, the correlation coefficient (R²) was 0.993 for the conductivity and 0.977 for the dissolved oxygen concentration. The root mean square error (RMSE) values for conductivity and dissolved oxygen concentration were 0.898 and 0.228 respectively.  After choosing the best model, the water quality parameters of the Itasy Lake were estimated on 25 May 2020. The conductivity ranged from 46.8 µS.cm-1 to 66.5 µS.cm-1 and the dissolved oxygen concentration from 6.5 mg.l-1 to 9.1 mg.l-1. These values indicate that the water from the Itasy Lake respects the Malagasy norms in terms of conductivity and dissolved oxygen concentration.

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Keywords: LSTM, Landsat8, Water quality , Itasy Lake

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Section: Original Research
Language : EN
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  1. Aslam M., Lee J.-M., Kim H.-S., Lee S.-J. & Hong S. (2020). Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgri Installation: A Comparative Study. Energies, vol. 13, no. 147, pp. 1–15. [Crossref]
  2. Azhar Jaffar , Norashikin M. Thamrin, Megat Syahirul Amin Megat Ali, Mohamad Farid Misnan & Ahmad Ihsan Mohd Yassin. (2022). Water Quality Prediction Using LSTM-RNN: A Review. Journal of Sustainability Science and Management, Volume 17 Number 7, July 2022: 204-225. [Crossref]
  3. Bazel Al-Shaibah, Xingpeng Liu, Jiquan Zhang, Zhijun Tong, Mingxi Zhang, Ahmed El-Zeiny, Cheechouyang Faichia, Muhammad Hussain & Muhammad Tayyab. (2021). Modeling Water Quality Parameters Using Landsat Multispectral Images: A Case Study of Erlong Lake, Northeast China. Remote Sensing, 13, 1603. [Crossref]
  4. Christopher Olah. (2015). Understanding LSTM Networks. 27 August 2015
  5. Clay Barrett D. & Amy E. Frazier. (2016). Automated Method for Monitoring Water Quality Using Landsat Imagery. Water, 8, 257. [Crossref]
  6. Dahl G. E., Sainath T. N., Hinton G. E. (2013). Improving deep neural networks for LVCSR using rectified linear units and dropout. International Conference on Acoustics, Speech and Signal Processing. [Crossref]
  7. Dhruti Dheda & Ling Cheng. (2020). A multivariate water quality parameter prediction model using recurrent neural network. 25 March 2020
  8. Haide Wang, Ji Zhou, Yizhao Wang, Jinlong Wei, Weiping Liu, Changyuan Yu and Zhaohui Li. (2019). Optimization Algorithms of Neural Networks for Traditional Time-Domain Equalizer in Optical Communications. Applied Sciences, 9, 3907. [Crossref]
  9. Hinton G. & Tieleman T. (2012). 5–RmsProp: Divide the Gradient by A Running Average of its Recent Magnitude. COURSERA, Neural Networks for Machine Learning, 4(2), pp. 26–31. [Crossref]
  10. Hochreiter S. & Schmidhuber J. (1997). Long short-term memory. Neural computation, 9(8):1735–1780. [Crossref]
  11. Huang C.J. & Kuo P.-H. (2018). A deep CNN-LSTM model for particulate matter (PM2.5) forecasting in smart cities. Sensors, 18, 2220. [Crossref]
  12. Kingma D.P. & Ba J.L. (2018). ADAM: A method for stochastic optimization. ICLR. [Crossref]
  13. Landsat8 Data Users Handbook. (2015). Retrieved March 29, from http://landsat.usgs.gov/documents/Landsat8 Users Handbook.pdf
  14. LeCun Y., Bengio Y. & Hinton, G. (2015). Deep learning. Nature, 521(7553): 436-444. [Crossref]
  15. Léonard Blier. (2017). Apprentissage profond et théorie de l’information. October 30, 2017
  16. Maryam Hafezparast Mavadat & Seiran Marab. (2021). Prediction of SAR and TDS parameters using LSTM- RNN model: A case study on Aran station, Iran. Journal of Applied Research in Water and Wastewater, 8 (2), 88-97. [Crossref]
  17. Mohammad Haji Gholizadeh, Assefa M. Melesse & Lakshmi Reddi. (2016). A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques. Sensors, 16, 1298. [Crossref]
  18. Muchlisin Arief. (2015). Development Of Dissolved Oxygen Concentration Extraction Model Using Landsat Data Case Study: Ringgung Coastal Waters. International Journal of Remote Sensing and Earth Sciences, Vol. 12 No.1. [Crossref]
  19. Murugesan A., Ramu A. & Kannan N. (2006). Water quality assessment from Uthamapalayam municipality in Theni District Tamil Nadu. India Pollution Research, 25:163-166. [Crossref]
  20. Nagy-Kovács Z., Davidesz J., Czihat-Mártonné K., Till G., Fleit E. & Grischek T.(2019). Water Quality Changes during Riverbank Filtration in Budapest, Hungary. Water, 11, 302. [Crossref]
  21. Nair V. & Hinton, G.E. (2010). Rectified linear units improve restricted Boltzmann machines. Haifa, pp. 807–814. [Crossref]
  22. Odubela Christiana. A, Balogun Wasiu A, Akinpelu T. A, Abioye Mayowa & Oluwe Musbau Olajide. (2021). Forecasting of water quality index using long short-term memory (LSTM) networks. International Journal of Advances in Engineering and Management (IJAEM), Volume 3, Issue 10 Oct 2021, pp: 1007-1017.[Crossref]
  23. Ping Liu., Jin Wang., Arun Kumar Sangaiah., Yang Xie & Xinchun Yin. (2019). Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment. Sustainability, 1, 2058. [Crossref]
  24. Rakotonirina Rija I., Randrianiaina Jerry J.C.F. & Lahatra Razafindramia Fils. (2020). Reinforcement of Knowledge of Some Parameters of Lake Itasy in Order to Establish Its Current State of Health. Resources and Environment, 10(2): 19-26. [Crossref]
  25. Rahim Barzegar, Mohammad Taghi Aalami & Jan Adamowski. (2020). Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model. Stoch Environ Res Risk Assess.[Crossref]
  26. Randrianiaina Jerry J. C. F., Rakotonirina Rija I., Ratiarimanana Jean R. & Lahatra Razafindramisa Fils. (2019). Modelling of Lake Water Quality Parameters by Deep Learning Using Remote Sensing Data. American Journal of Geographic Information System, 8(6): 221-227. [Crossref]
  27. Song C., Woodcock C.E., Seto K.C., Lenney M.P. & Macomber S.A. (2001). Classification and change detection using Landsat TM data: when and how to correct atmospheric effects. Remote Sensing of Environment, 75, pp. 230-244. [Crossref]
  28. Wang Y., Zhou J., Chen K., Wang Y. & Liu L. (2017). Water quality prediction method based on LSTM neural network. In Proceedings of the 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), Nanjing, China, 24–26. pp. 1–5. [Crossref]
  29. Xianhe Wang, Ying Li, Qian Qiao, Adriano Tavares & Yanchun Liang. (2023). Water Quality Prediction Based on Machine Learning and Comprehensive Weighting Methods. Entropy, 25, 1186. [Crossref]
  30. Zeiler M. D., Ranzato M., Monga R., Mao M., Yang K., Le Q. V. & Hinton G.E. (2013). On rectified linear units for speech processing. International Conference on Acoustics, Speech and Signal Processing, pp. 3517–3521. [Crossref]

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