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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
An Efficient Methodology for Water Supply Pipeline Risk Index Prediction for Avoiding Accidental Losses
Digital Object Identifier (DOI) : 10.14569/IJACSA.2020.0110551
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 5, 2020.
Abstract: 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).
Keywords: Neural networks; normalization; risk index; mean square error; statistical moments
Muhammad Shuaib Qureshi, Ayman Aljarbouh, Muhammad Fayaz, Muhammad Bilal Qureshi, Wali Khan Mashwani and Junaid Khan, “An Efficient Methodology for Water Supply Pipeline Risk Index Prediction for Avoiding Accidental Losses” International Journal of Advanced Computer Science and Applications(IJACSA), 11(5), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110551
@article{Qureshi2020,
title = {An Efficient Methodology for Water Supply Pipeline Risk Index Prediction for Avoiding Accidental Losses},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110551},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110551},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {5},
author = {Muhammad Shuaib Qureshi and Ayman Aljarbouh and Muhammad Fayaz and Muhammad Bilal Qureshi and Wali Khan Mashwani and Junaid Khan}
}
