An Efficient Methodology for Water Supply Pipeline Risk Index Prediction for Avoiding Accidental Losses

The accidents happening to buildings and other human facilitation sectors due to poor water supply pipelining system is a random phenomenon, but an efficient estimation system can help to escape from such accidents. Such a system can be useful in assisting the caretakers to take the initiative measures to avoid the occurrence of the accidents or at least reduce the associated risk. In this paper, we target this issue by proposing a water supply pipelines risk estimation methodology using feed forward backpropagation neural network (FFBPNN). For validation and performance evaluation, real data of water supply pipelines collected in Seoul, Republic of South Korea from 1987 to 2010 is used. A comprehensive analysis is performed in order to get reasonable results with both original and pre-processed input data. Pre-processing consists of two steps: data normalization and statistical moments computation. Statistical moments are mean, variance, kurtosis and skewness. Significant improvement in prediction accuracy is observed with data pre-processing in terms of selected performance metrics, such as mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean squared error (RMSE).

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