  The global degradation of water resources is a matter of great concern,
especially for the survival of humanity. The effective monitoring and
management of existing water resources is necessary to achieve and maintain
optimal water quality. The prediction of the quality of water resources will
aid in the timely identification of possible problem areas and thus increase
the efficiency of water management. The purpose of this research is to develop
a water quality prediction model based on water quality parameters through the
application of a specialised recurrent neural network (RNN), Long Short-Term
Memory (LSTM) and the use of historical water quality data over several years.
Both multivariate single and multiple step LSTM models were developed, using a
Rectified Linear Unit (ReLU) activation function and a Root Mean Square
Propagation (RMSprop) optimiser was developed. The single step model attained
an error of 0.01 mg/L, whilst the multiple step model achieved a Root Mean
Squared Error (RMSE) of 0.227 mg/L.
