Water quality prediction using deep learning.

Water quality prediction has a crucial significance in ensuring the safety and usability of water resources. In this paper, we propose a CNN with BiLSTM model for accurate water quality prediction. The model combines convolutional neural network layers with BiLSTM to effectively capture the spatio-temporal dependencies present in the data. Experimental results on the water quality dataset demonstrate the superior performance of the proposed model. The model achieves a validation accuracy of 92.83%. These results surpass those obtained by other models such as GRU, LSTM, and CNN with LSTM. The CNN with BiLSTM model's ability to effectively learn and classify water samples based on provided features highlights its potential for reliable water quality prediction. This paper contributes to the field of water quality prediction by providing a robust model that can aid in ensuring the safety and suitability of water resources. [ABSTRACT FROM AUTHOR]