Abstract:
Water is the secret of life and makes up almost 70% of the Earth’s surface. It has become necessary to protect the water
resources around us from pollution and neglect, which can result in the loss of life and health. Artificial intelligence (AI) has the
potential to improve water quality analysis, forecasting, and monitoring systems for sustainable and environmentally friendly water
resource management. As a result, this work focuses on the prediction of accurate and sustainable water quality prediction model using
hybridization between supervised and unsupervised machine learning techniques. A set of multi-model learning features was used to
represent the state of the water and determine its suitability category (i.e., safe or unsafe). This is done by building a hybrid model
between supervised algorithms (LGBM) and unsupervised algorithms (COPOD, IForest, and CBLOF) after fusing their outliers, and the
proposed model is called (HLGBM+Fusion CIC). Also, the Gamel herd swarm optimization algorithm was applied to find the optimum
hyper-parameters. The models were evaluated with or without class balancing and compared in terms of accuracy, recall, precision, f1
score, and area under the curve (AUC). The results showed that the proposed model (HLGBM+Fusion CIC) outperformed other models
by 99.2% in accuracy, AUC, and f1-score. Also, it achieved 99% precision and 99.3% recall. Finally, this paper presented a framework
for researchers using hybrid machine learning to forecast water quality.