A hybrid AI model integrating LSTM, XGBoost, and K-means for interpretable prediction and clustering of water quality in data-scarce regions

Abstract Water quality degradation in data-scarce and pollution-prone regions such as the Niger Delta poses serious health and ecological risks. Traditional monitoring methods are limited by cost, temporal gaps, and lack of interpretability. This study develops a hybrid artificial intelligence (AI) framework integrating Long Short-Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), and K-Means clustering for interpretable water-quality prediction and pattern discovery in under-monitored environments. The framework addresses the scarcity of temporal datasets by adapting LSTM to static physicochemical data through pseudo-sequential encoding, while XGBoost enhances regression and classification accuracy in…