DEVELOPMENT OF AN INTERNET OF THINGS BASED WATER MANAGEMENT SYSTEM USING DECISION TREE AND DEEP NEURAL NETWORK ALGORITHMS

Abstract

Distribution of Water has been a major source of concern all over the world. Despite the fact that water is a scarce commodity, a lot of human activities in terms of poor management such as opening taps when not needed and careless attitudes towards broken pipes contribute to poor distribution. Furthermore, the supply of the commodity at constant pressure to areas when not needed contributes to little or no supply to where it is needed. This is because; a lot is wasted without being used as a result of leaks and these human activities. This necessitates the need for a system to manage water distribution effectively. To this end, this research presents the Development of an Internet of Things based Water Management system using Decision Tree and Deep Neural Network algorithms. To accomplish this research work, an efficient IoT water meter was developed to take consumption data from MI Wushishi Minna, which is our area of interest. The data generated was analyzed to understand the pattern of demand. Furthermore, a water tank capable of supplying the study area was simulated having constant valve resistance on Simulink. Based on the consumption behavior of the occupants of the estate, another simulation was done using Simulink in which the valve resistance was varied based on the demand. This results to saving water of about 3000 liters. To make the system smart, Deep neural Decision tree algorithm was used to achieve auto selection via classification. Compared to other existing work, the scheduling achieved via Decision Tree Algorithm in this research had an improved accuracy of 94.2%.