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The global degradation of water resources is a matter of great concern,especially for the survival of humanity. The effective monitoring andmanagement of existing water resources is necessary to achieve and maintainoptimal water quality. The prediction of the quality of water resources willaid in the timely identification of possible problem areas and thus increasethe efficiency of water management. The purpose of this research is to developa water quality prediction model based on water quality parameters through theapplication of a specialised recurrent neural network (RNN), Long Short-TermMemory (LSTM) and the use of historical water quality data over several years.Both multivariate single and multiple step LSTM models were developed, using aRectified Linear Unit (ReLU) activation function and a Root Mean SquarePropagation (RMSprop) optimiser was developed. The single step model attainedan error of 0.01 mg/L, whilst the multiple step model achieved a Root MeanSquared Error (RMSE) of 0.227 mg/L.

A multivariate water quality parameter prediction model using recurrent neural network