Decision Tree Analysis Approaches to Classify Sensors Data in a Water Pumping Station

Abstract

Water pumping stations play a vital role in the citizens life where the failure in the pumping schedule, or the quality of the pumping may affect the life of the citizens. The data of the water pumping station may expose the weakness points in the system of the station where they can be overcome using machine learning approaches. In this paper, six decision tree algorithms are examined to find the optimal one for classifying the data of water pumping stations. The main goal is to determine the fault in the sensors to control the pumping process and to overcome the future failure. Six algorithms namely (J48, Rep Tree, Random Forest, Decision Stump, Hoeffding Tree, and Random Tree) are examined before and after implementing feature selection (FS) process. FS is implemented to find the most correlated sensors that remove the less correlated sensors. FS process affects the accuracies of the algorithms where it enhances the resulting accuracies of the algorithms. Random Forest and Random Tree algorithms prove their accuracy in data classification with 100% after implementing FS and removing the less correlated sensors data. The model can be used as assistant tool for classifying and predicting the failure in water pumping station