Comparable IoT and DL methods of drinking water usage

IoT applications have actively employed advanced technologies, utilising neural networks to recognize, analyse, and interact with their surroundings. Significantly, Amazon Echo exemplifies an IoT application by bridging physical and human realms with the digital domain, employing deep learning for voice command comprehension. Similarly, Microsoft's Windows facial recognition security system integrates DL to unlock doors upon facial recognition. This research explores the integration of IoT with DL techniques to enhance the monitoring and analysis of drinking water quality assessment, evaluating various methods to determine drinkability. Various machine learning algorithms, including Random Forest, LightGBM and Bagging Classifier, are employed to predict water quality based on multiple parameters such as pH, conductivity and turbidity. The study uses a comprehensive data set featuring 3277 values for nine different water quality indicators. Comparative analysis revealed similar outcomes: Random Forest demonstrated the highest accuracy, achieving a predictive accuracy of 0.824695 followed by Light GBM and Bagging Classifier. This research contributes to the ongoing efforts to employ advanced computational techniques in environmental monitoring, providing a reliable methodological framework for future studies to enhance water quality assessment.