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Predicting water quality is a significant area of study in the field of smart water technology, since it may provide valuable assistance in managing and mitigating water pollution. Due to the increasing global population and the need for effective methods of agriculture and irrigation, there is a continuous increase in the demand for water, which lead to a scarcity of water resources. Consequently, smart water management systems have been created with the objective of enhancing the effectiveness of water management. Nevertheless, conventional water quality prediction models mostly use data-driven approaches and only depend on diverse sensor data. In recent research, deep learning algorithms have been extensively used for water quality prediction due to their robust ability to map highly nonlinear connections while maintaining acceptable computational efficiency. Therefore, the LSTM-CN model presented in …