Hybrid DE optimised kernel SVR-relied techniques to forecast the outlet turbidity and outlet dissolved oxygen in distinct filtration media and micro-irrigation filters
dc.contributor.author
dc.date.accessioned
2024-05-13T10:47:03Z
dc.date.available
2024-05-13T10:47:04Z
dc.date.issued
2024-05-13
dc.identifier.issn
1537-5110
dc.identifier.uri
dc.description.abstract
In micro-irrigation systems, distinct media filters and filtering materials are employed to remove suspended solids from irrigation water and thereby avoid emitter obstruction. Turbidity is related to suspended solids and dissolved oxygen depends on organic matter load. At this time, no models exist that are trustworthy enough to forecast the dissolved oxygen and turbidity at the outlet when utilising various media configurations and filter types. The objective of this investigation was to construct a model that can identify turbidity and dissolved oxygen at the filter outlet in advance. This study presents an algorithm for meta-heuristic optimisation inspired by populations termed Differential Evolution (DE) in conjunction with Support Vector Regression (SVR) (DE/SVR-relied model). This is an effective machine learning method, with seven kernel types for calculating the output turbidity (Turbo) and the output dissolved oxygen (DOo) from a dataset comprising 1,016 samples of various reclaimed water-using filter types. The type of media and filter, the height of the filter bed, the cycle duration, and the filtration velocity, as well as the electrical conductivity at the filter inlet, pH, inlet dissolved oxygen, water temperature, and the input turbidity are all tracked and analysed in order to achieve this. The best-fitted DE/SVR-relied model was constructed to predict the Turbo and DOo as well as the input variables' relative importance. Determination coefficients for the best-fitted DE/SVR-relied model for the testing dataset were 0.89 and 0.92 for outlet turbidity (Turbo) and outlet dissolved oxygen (DOo), respectively, showing a good predictive performance which are of great importance for the management of drip irrigation systems
dc.format.extent
15 p.
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.relation.isformatof
Reproducció digital del document publicat a: https://doi.org/10.1016/j.biosystemseng.2024.04.020
dc.relation.ispartof
Biosystems Engineering, 2024, vol. 243, p. 42-56
dc.rights
Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional
dc.rights.uri
dc.source
García Nieto, P. J. García-Gonzalo, E. Arbat Pujolràs, Gerard Duran i Ros, Miquel Pujol i Sagaró, Toni Puig Bargués, Jaume 2024 Hybrid DE optimised kernel SVR-relied techniques to forecast the outlet turbidity and outlet dissolved oxygen in distinct filtration media and micro-irrigation filters Biosystems Engineering 243 42 56
dc.title
Hybrid DE optimised kernel SVR-relied techniques to forecast the outlet turbidity and outlet dissolved oxygen in distinct filtration media and micro-irrigation filters
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.identifier.idgrec
038818
dc.type.peerreviewed
peer-reviewed
dc.identifier.eissn
1537-5129