Modelling Itasy Lake Water Quality by Long Short Term Memory (LSTM) using Landsat8 Data - Frederick, Randrianiaina Jerry Jean Christien; Itokiana, Rakotonirina Rija; Rasoloariniaina, Jean Robertin; Razafindramisa, Fils Lahatra (Department of Urban and Regional Planning, Diponegoro University, 2025)
Modeling lake water quality is very important to preserve and protect this resource. Several algorithms can be used to model lake water quality using in-situ measurement data. This work used The Long Short-Term Memory (LSTM) deep learning (DL) architecture to obtain models for modeling and predicting water quality parameters of Lake Itasy depending on the reflectance of Landsat8 OLI. The main purpose of this study was to identify the appropriate LSTM model in function of the optimization algorithms: Adagrad, RMSprop and Adam, in order to do the estimation on the date provided, according to the date of satellite image acquisition. The obtained results showed the performance of the developed LSTM model, with an Adaptive Moment Estimation (Adam) optimization algorithm that provided 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 Lake Itasy were estimated on May 25th 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 to 9.1 mg/L. These values indicate that the water from Lake Itasy respects the Malagasy norms in terms of conductivity and dissolved oxygen concentration