Pipe Failure Prediction

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                                                                 Pipe breaks in urban water distribution network lead to significant economic and social costs, putting the service quality as well as the profit of water utilities at risk. In this method we use Data mining prediction method to detect pipe failures. For this prediction they used raw dataset provided by the water utility consists of records of over 500,000 water pipes, adding up to almost 6,000 kilometers in length. In here pipe break prediction task as a ranking problem, which tries to rank pipes according to their break risks, without estimating the true risks.
                                                               RankBoost.B Algorithm is used for this method among large set of algorithms such as KNN, SVM etc. In this algorithm, within each iteration, it finds a weak learner to rank the weighted instances at first, and then updates the weight of each instance according to the performance of the weak learner. Like any other boosting-type algorithms, RankBoost.B builds a strong model in an iterative manner.

Finally, these weak learners are linearly combined to form a strong ranking model.

 Reference :http://ieeexplore.ieee.org/document/6544910/

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