CONDITION PREDICTION MODELS OF DETERIORATED TRUNK SEWER USING MULTINOMIAL LOGISTIC REGRESSION AND ARTIFICIAL NEURAL NETWORK

Sewer systems are used to convey sewage and/or storm water to sewage treatment plants for disposal by a network of buried sewer pipes, gutters, manholes and pits. Unfortunately, the sewer pipe deteriorates with time leading to the collapsing of the pipe with traffic disruption or clogging of the pipe causing flooding and environmental pollution. Thus, the management and maintenance of the buried pipes are important tasks that require information about the changes of the current and future sewer pipes conditions. In this research, the study was carried on in Baghdad, Iraq and two deteriorations model's multinomial logistic regression and neural network deterioration model NNDM are used to predict sewers future conditions. The results of the deterioration models' application showed that NNDM gave the highest overall prediction efficiency of 93.6% by adapting the confusion matrix test, while multinomial logistic regression was inconsistent with the data. The error in prediction of related model was due to its inability to reflect the dependent variable (condition classes) ordered nature.