Towards Precise Water Quality Assessment : A Deep Learning Approach with Feature Selection in Smart Monitoring Systems

Jay Dave; Dr. Ajay Patel; Dr. Hitesh Raval

doi:10.32628/CSEIT241045

Authors

Jay Dave Department of Computer Science, Ganpat University, Kherva, Gujarat, India Author
Dr. Ajay Patel Department of Computer Science, Ganpat University, Kherva, Gujarat, India Author
Dr. Hitesh Raval Department of Computer Science, Sankalchand Patel University, Visnagar, Gujarat, India Author

DOI:

https://doi.org/10.32628/CSEIT241045

Keywords:

CNN, KNN, ANN, XGBoost, Inception V3, Smart Water Quality Monitoring System

Abstract

As water quality concerns intensify, the imperative for accurate monitoring systems grows. This study pioneers a novel approach to precise water quality assessment by integrating deep learning techniques and feature selection in smart monitoring systems. Utilizing k-Nearest Neighbors (KNN), Convolutional Neural Network (CNN), and Inception V3 for classification, along with Random Forest, AdaBoost, and XGBoost for feature selection, the study presents a detailed examination of their performance on water quality datasets. The results showcase notable improvements in both training and testing accuracies for KNN when coupled with Random Forest and varying numbers of estimators. The combination of CNN and AdaBoost exhibits robust performance, underscoring the impact of feature extraction on training and testing accuracies. Inception V3, when integrated with XGBoost, demonstrates nuanced results, emphasizing the significance of feature extraction in enhancing classification outcomes. Specifically, the performance metrics reveal a fusion model using XGBoost and Inception V3 achieving an accuracy of 65.82%, surpassing individual models like Inception V3 (60.05%). Similarly, the combination of AdaBoost and CNN attains a performance of 65.17%, outperforming individual models such as CNN (64.32%). Additionally, the integration of Artificial Neural Networks (ANN) with Random Forest yields a performance of 69.05%, showcasing improvement over standalone ANN (55.79%). The findings underscore the efficacy of deep learning models, particularly when integrated with appropriate feature selection algorithms, in advancing the precision of water quality assessment in smart monitoring systems. This study contributes valuable insights to the field of environmental monitoring, providing a basis for further exploration of synergies between deep learning and feature selection for enhanced accuracy in water quality assessment. The proposed approach holds promise for addressing the critical challenge of precise water quality monitoring in the face of escalating environmental concerns.

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