AN EFFICIENT IOT-BASED PREDICTION SYSTEM TO PREDICT THE SUITABILITY OF WATER USAGE AND THE METHOD THEREOF

This disclosure presents an efficient IoT-based prediction system for water classification, specifically designed for assessing water quality according to the recommended best use by the Ministry of Hydrology and Water Resources Information Department in India. The system utilizes a Modified Deep Learning Neural Network (MDLNN) classifier and a novel adaptive incremental learning framework. It consists of six components: real-time data collection through sensor devices, data cleaning to remove outliers and missing values, feature selection using forward feature selection method, handling of imbalanced data with an improved data augmentation technique and G-SMOTE, and water quality classification using the MDLNN classifier. Additionally, an adaptive incremental learning framework is employed to handle unseen data. Experimental results demonstrated an impressive accuracy of 99.34% and validation loss of 0.0415, addressing the challenge of imbalanced water quality classes. Overall, this approach effectively utilizes multi-class classification for water quality assessment.