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- W4381299729 abstract "In this study, natural language processing is proposed to automatize the extraction of information from an extensive number of scientific manuscripts on the use of machine learning in the water industry. Concretely, the articles’ search focuses on the literature published between 2013 and 2022 (both years included) that present or talk about the implementation of machine learning techniques to predict pipe failures in water distribution networks. For this purpose, it is a condition for the papers gathered to include the terms ‘pipe failure’, ‘water distribution or supply’ and ‘machine learning’, among others. The study discusses three aspects: (1) the use of different machine learning models; (2) some characteristics about the data processing, training, and validation phases; and (3) the variables or factors related to water distribution networks that are more popular according to the collected papers. This discussion is performed by analyzing the frequency of appearance of certain terms in the documents. Furthermore, the connection between the frequencies of a pair of terms is examined by using scatter plot graphs." @default.
- W4381299729 created "2023-06-21" @default.
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- W4381299729 date "2023-01-01" @default.
- W4381299729 modified "2023-09-30" @default.
- W4381299729 title "A Literature Review on Machine Learning to Optimize Water Network Management Using Natural Language Processing" @default.
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- W4381299729 doi "https://doi.org/10.1007/978-981-99-1919-2_1" @default.
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