Neutrosophic rule-based prediction system for assessment of pollution on Benthic Foraminifera in Burullus Lagoon in Egypt

Lake Burullus is one of the most important coastal lakes in Egypt. They are subject to industrial waste as well as agricultural and household effluents. The modern foraminifera was used to reflect the health of the ecosystem they inhabit. This paper used a Neutrosophic Rule-based Classification System (NRCS) to predict the pollution status of Burullus lagoon according to the concentrations of trace metals. The proposed system was trained by different ecological parameters which were chosen by a geological expert. Thirty samples of surface sediments were collected from Lake Burullus to assess the responsiveness of the fuaminvera bottom trace metal elements. Some samples were used for training the NRCS model (based on different selected geological parameters), while the other samples were kept hidden for testing the efficiency of the system. Several experiments have been done to validate the performance of the NRCS system. Three different sets of ecological parameters were tested by NRCS which showed high prediction accuracy results for the three types of parameters, 92%, 93% and 100%, respectively. Also, the NRCS showed advances compared to other well-known classifiers for all the three sets of parameters.