Hybrid Intelligent Technique between Supervised and Unsupervised Machine Learning to Predict Water Quality

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.