Abstract:
The global concern regarding water distribution persists due to its scarcity and mismanagement. Human behaviors, such as unnecessary tap usage and neglect of broken pipes, exacerbate the issue of inadequate water distribution. Additionally, the constant supply of water at undesired locations contributes to wastage. This research addresses the necessity for an effective water distribution management system. In response, an Internet of Things (IoT) based Water Management system is developed, employing Decision Tree and Deep Neural Network algorithms. The study involves creating an IoT water meter to collect consumption data from MI Wushishi Minna, the selected area of focus. Analysis of the data sheds light on consumption patterns. Moreover, a water tank model with consistent valve resistance is simulated using Simulink to cater to the study area’s needs. Altering valve resistance based on demand is simulated, resulting in potential water savings of approximately 3000 liters. The integration of the Deep Neural Decision Tree algorithm enhances the system’s intelligence by enabling automatic selection through classification. Notably, the Decision Tree Algorithm’s scheduling accuracy in this research improves to 94.2% compared to existing approaches.
Creating a Water Management System Based on Internet of Things Utilizing Decision Tree and Deep Neural Network Algorithm,  GET MORE, ACTUARIAL SCIENCE PROJECT TOPICS AND MATERIALS
