title: Performance study of the application of Artificial Neural Networks to the completion and prediction of data retrieved by underwater sensors.
abstract: This paper presents a proposal for an Artificial Neural Network (ANN)-based architecture for completion and prediction of data retrieved by underwater sensors. Due to the specific conditions under which these sensors operate, it is not uncommon for them to fail, and maintenance operations are difficult and costly. Therefore, completion and prediction of the missing data can greatly improve the quality of the underwater datasets. A performance study using real data is presented to validate the approach, concluding that the proposed architecture is able to provide very low errors. The numbers show as well that the solution is especially suitable for cases where large portions of data are missing, while in situations where the missing values are isolated the improvement over other simple interpolation methods is limited.
authors.affiliation: Universidad de Valladolid, Dpto. TSyCeIT, ETSIT, Paseo de Belén 15, 47011 Valladolid, Spain. cbalzor@ribera.tel.uva.es
authors.forename: Carlos
authors.initials: C
authors.lastname: Baladrón
authors.forename: Javier M
authors.initials: JM
authors.lastname: Aguiar
authors.forename: Lorena
authors.initials: L
authors.lastname: Calavia
authors.forename: Belén
authors.initials: B
authors.lastname: Carro
authors.forename: Antonio
authors.initials: A
authors.lastname: Sánchez-Esguevillas
authors.forename: Luis
authors.initials: L
authors.lastname: Hernández
mesh_terms: D016208:Databases, Factual; D004867:Equipment Design; D019544:Equipment Failure Analysis; D016247:Information Storage and Retrieval / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D058998:Remote Sensing Technology / Q000295:instrumentation* / Q000379:methods; D014159:Transducers; D014867:Water / Q000032:analysis*
chemical_list: D014867:Water