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dc.contributor.author | García, José![]() |
es_ES |
dc.contributor.author | Leiva-Araos, Andrés![]() |
es_ES |
dc.contributor.author | Diaz-Saavedra, Emerson![]() |
es_ES |
dc.contributor.author | Moraga, Paola![]() |
es_ES |
dc.contributor.author | Pinto, Hernan![]() |
es_ES |
dc.contributor.author | Yepes, V.![]() |
es_ES |
dc.date.accessioned | 2024-05-23T18:05:49Z | |
dc.date.available | 2024-05-23T18:05:49Z | |
dc.date.issued | 2023-11 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/204393 | |
dc.description.abstract | [EN] Water infrastructure integrity, quality, and distribution are fundamental for public health, environmental sustainability, economic development, and climate change resilience. Ensuring the robustness and quality of water infrastructure is pivotal for sectors like agriculture, industry, and energy production. Machine learning (ML) offers potential for bolstering water infrastructure integrity and quality by analyzing extensive data from sensors and other sources, optimizing treatment protocols, minimizing water losses, and improving distribution methods. This study delves into ML applications in water infrastructure integrity and quality by analyzing English-language articles from 2015 onward, compiling a total of 1087 articles. Initially, a natural language processing approach centered on topic modeling was adopted to classify salient topics. From each identified topic, key terms were extracted and utilized in a semi-automatic selection process, pinpointing the most relevant articles for further scrutiny, while unsupervised ML algorithms can assist in extracting themes from the documents, generating meaningful topics often requires intricate hyperparameter adjustments. Leveraging the Bidirectional Encoder Representations from Transformers (BERTopic) enhanced the study¿s contextual comprehension in topic modeling. This semi-automatic methodology for bibliographic exploration begins with a broad topic categorization, advancing to an exhaustive analysis of each topic. The insights drawn underscore ML¿s instrumental role in enhancing water infrastructure¿s integrity and quality, suggesting promising future research directions. Specifically, the study has identified four key areas where ML has been applied to water management: (1) advancements in the detection of water contaminants and soil erosion; (2) forecasting of water levels; (3) advanced techniques for leak detection in water networks; and (4) evaluation of water quality and potability. These findings underscore the transformative impact of ML on water infrastructure and suggest promising paths for continued investigation. | es_ES |
dc.description.sponsorship | Víctor Yepes is supported by Grant PID2020-117056RB-I00 funded by MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Applied Sciences | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Water infrastructure integrity | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | Environmental sustainability | es_ES |
dc.subject | Natural language processing | es_ES |
dc.subject | BERTopic | es_ES |
dc.subject.classification | INGENIERIA DE LA CONSTRUCCION | es_ES |
dc.title | Relevance of Machine Learning Techniques in Water Infrastructure Integrity and Quality: A Review Powered by Natural Language Processing | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/app132212497 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-117056RB-I00/ES/OPTIMIZACION HIBRIDA DEL CICLO DE VIDA DE PUENTES Y ESTRUCTURAS MIXTAS Y MODULARES DE ALTA EFICIENCIA SOCIAL Y MEDIOAMBIENTAL BAJO PRESUPUESTOS RESTRICTIVOS/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Técnica Superior de Ingenieros de Caminos, Canales y Puertos - Escola Tècnica Superior d'Enginyers de Camins, Canals i Ports | es_ES |
dc.description.bibliographicCitation | García, J.; Leiva-Araos, A.; Diaz-Saavedra, E.; Moraga, P.; Pinto, H.; Yepes, V. (2023). Relevance of Machine Learning Techniques in Water Infrastructure Integrity and Quality: A Review Powered by Natural Language Processing. Applied Sciences. 13(22). https://doi.org/10.3390/app132212497 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/app132212497 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 13 | es_ES |
dc.description.issue | 22 | es_ES |
dc.identifier.eissn | 2076-3417 | es_ES |
dc.relation.pasarela | S\503967 | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.subject.ods | 09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación | es_ES |