Artificial intelligence and big data approaches to water quality management for environmental sustainability

Maintaining the quality of water is crucial for preserving human wellness, and the ecosystem, as well as controlling pollution. Artificial intelligence (AI) has the potential to improve water quality forecasting and classification. Research evaluates AI algorithms for effectively predicting water quality based on big data. AI and big data analytics have emerged as promising solutions, offering enhanced predictive accuracy, real-time monitoring, and intelligent decision support. The objective was to predict water quality using an Adaptive sunflower optimized isolation forest (ASO-IsoForest). The water quality dataset provides significant information for environment management and sustainability. Min-max normalization was used to preprocess the collected data. The features such as Hardness, pH value, Chloramines, Solids, and Conductivity were extracted using Discrete wavelet transform (DWT). The result shows the effectiveness of ASO-IsoForest in improving the quality of water forecasting and long-term water resource management, encouraging pollution reduction and water preservation. Experimental results demonstrate that ASO-IsoForest achieves a precision of 94.36%, accuracy of 92.25%, F1-score of 92.35%, and recall of 93.16%, outperforming conventional machine learning models. Machine Learning (ML) approach offers real-time monitoring, improved accuracy, cost-effectiveness, resource optimization, partial data handling, and improved access to clean water for early warning systems and timely interventions. The framework utilizes AI techniques and big data to enhance sustainable water management practices and facilitate timely pollution control interventions. The research highlights the potential of intelligent models in safeguarding freshwater resources, reducing environmental risks, and ensuring equitable access to clean water, highlighting the ASO-IsoForest framework's transformative potential for environmental governance and sustainable development.