An efficient IoT based prediction system for classification of water using novel adaptive incremental learning framework

Creating an adaptive, accurate, and reliable model is a universal problem. Machine learning models give poor accuracy on unseen data, and therefore, the testing accuracy of the trained model is affected. This study presents a novel adaptive incremental learning framework for IoT based smart water quality classification system to predict the suitability of water for different applications. Initially, water quality data is collected using IoT sensors. After that, data cleaning is performed by removing missing values and outliers. Next, features associated with the sensed data are obtained, and unwanted features are removed. Then, the G-SMOTE technique is proposed, which hybridizes the SMOTE and the genetic algorithm to address the imbalanced data set problem. After that, the multi-class classification is performed using a modified deep learning neural network classifier which uses hyperparameter tunning technique to obtain better accuracy with minimum validation loss. Finally, the study presents a novel framework for adaptive incremental learning on unseen data. Experimental result shows that our method presents a new state-of-the-art multi-class water quality classification method with an accuracy of 99.34% and validation loss of 0.0415.