Aquify:AI-Enhanced Predictive Analytics Toolkit For Water Contamination

Effective monitoring of water quality remains a problem, in real-time contamination detection and prediction. Despite advancements in the sector, many existing approaches continue to rely on labor-intensive laboratory testing, which limits their capacity to give rapid and accurate results. By leveraging advanced ML/DL models, it is possible to build a more robust and adaptable platform for real-time analysis, prediction, and decision support. Approaches based on models such as Random Forest, XGBoost, CNN, RNN, LSTM, and GRU have been good with the accuracy of water quality predictions. They help identify pollution sources and diagnose water quality issues, providing essential information to stakeholders in sustainable water resource management. The ML and DL approaches help improve water quality monitoring and boost future research in environmental sustainability and natural resource management.