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      Sewer Condition Prediction and Analysis of Explanatory Factors

      , , ,
      Water
      MDPI AG

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          Abstract

          Sewer condition is commonly assessed using closed-circuit television (CCTV) inspections. In this paper, we combine inspection results, pipe attributes, network data, and data on pipe environment to predict pipe condition and to discover which factors affect it. We apply the random forest algorithm to model pipe condition and assess the variable importance using the Boruta algorithm. We analyse the impact of predictor variables on poor condition using partial dependence plots, which are a valuable technique for this purpose. The results can be used in screening pipes for future inspections and provide insight into the dynamics between predictor variables and poor condition.

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          Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar

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            Factors influencing the structural deterioration and collapse of rigid sewer pipes

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              Markov Model for Storm Water Pipe Deterioration

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                Author and article information

                Journal
                WATEGH
                Water
                Water
                MDPI AG
                2073-4441
                September 2018
                September 13 2018
                : 10
                : 9
                : 1239
                Article
                10.3390/w10091239
                afc7b0c0-d24b-4db9-ab24-4c6abb72ff4f
                © 2018

                https://creativecommons.org/licenses/by/4.0/

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