Deep learning based an effective hybrid model for water quality assessment

Water, which is very important for life and civilizations on Earth, has been a source of life for all living things. However, freshwater resources gradually decrease due to climate change, pollution, and population growth. Water pollution is the quality changes that occur due to various activities of people, change the chemical, biological, and physical properties of water, restrict or prevent its use, and disrupt ecological balances. The water quality criteria determined to keep this pollution under control ensure the safe use of water. Water quality observations have gained more importance today due to intense environmental concerns and water pollution. This situation revealed the necessity of water quality assessment for water source use. In this study, the CNN-LSTM-based hybrid model was proposed to assess water quality. The proposed model was compared with AdaBoost, DT, GNB, kNN, LGBM, RF, and LSTM according to accuracy, precision, recall, F-score, and AUC. The proposed model has 98.81% accuracy, 99.03% precision, 99.65% recall, 99.33% F-score, and 93% AUC. Practitioner Points: CNN-LSTM hybrid model was developed to water quality assessment. The proposed model is compared with established techniques like AdaBoost, DT, GNB, kNN, LGBM, RF, and LSTM. The developed model has 98.81% classification accuracy. Experimental results show that the developed model will ensure the use of safe water according to water quality criteria.