The potential of novel hybrid SBO-based long short-term memory network for prediction of dissolved oxygen concentration in successive points of the Savannah River, USA.

The accurate estimation of dissolved oxygen (DO) as an important water quality indicator can provide a basis for ensuring the preservation of the riverine ecosystem and designing proper water quality development plans. Therefore, this study aimed to propose a novel hybrid model based on long short-term memory (LSTM) networks with Satin Bowerbird optimizer (SBO) algorithm for the estimation of the DO concentration based on multiple water quality parameters. Furthermore, to compare the supreme performance of proposed hybrid model, standalone LSTM, support vector machine (SVM) and Gaussian process regression (GPR) were employed. The models were prepared using the datasets collected from three successive gauging stations along the Savannah River, USA, for the period 2015-2021. The modeling process was performed through local and cross-station scenarios to assess the interrelations between the DO values of upstream/downstream stations. The comparison of estimation accuracies of different employed models revealed that the proposed SBO-LSTM yields a correlation coefficient (R) of 0.981, Nash-Sutcliffe efficiency (NSE) of 0.957, and root mean square error (RMSE) of 0.034 for a test series of dissolved oxygen series which was the most accurate model through both local and cross-station scenarios. Also, the proposed SBO-LSTM model showed better performance by 0.52% and 1.26% than employed SVM and GPR models, respectively. The obtained results showed the essential role of the water temperature parameter in the DO modeling of all three studied stations.