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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