Robust learning algorithm based on agreement among soil sampling techniques

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dc.authoridTutmez, Bulent/0000-0002-2618-3285
dc.contributor.authorTutmez, Bulent
dc.date.accessioned2024-08-04T20:53:30Z
dc.date.available2024-08-04T20:53:30Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractEnvironmental investigation and modelling require case-based sampling techniques in various domains such as soil, air and water as well as living populations. In most cases, a limited number of sampling techniques can be conducted into a site stemming from the impracticability of geology, time and cost. In addition, if some outliers are recorded due to natural variability and the metrological issues, the modelling process is in need of robust analysis tools. Therefore, a robustness-based sampling agreement and vigorous estimations are needed. The primary purpose of this study is to provide a consensus between different soil sampling methods when a merging is required and to make reliable estimations in case of the existence of any outlier. A machine learning algorithm has been established for reaching targets by considering robustness, transparency, accuracy as well as reproducibility. The algorithm is suited for small data sets and all steps of the algorithm demonstrated that the robust learning algorithm is not severely influenced by the presence of a few outliers. The testing performed based on regression discontinuity analysis and comparative estimations also showed that repeated double robust regression outperforms the conventional multiple least-squares regression. Thus, the learning algorithm can be recommended to the fields of environmental sciences and also may be considered in different disciplines with minor adaptations. (c) 2023 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.asoc.2023.110123
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85149168901en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.asoc.2023.110123
dc.identifier.urihttps://hdl.handle.net/11616/101200
dc.identifier.volume137en_US
dc.identifier.wosWOS:000995887300001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSamplingen_US
dc.subjectRobust regressionen_US
dc.subjectMachine learningen_US
dc.subjectDissimilarityen_US
dc.subjectRegression discontinuityen_US
dc.titleRobust learning algorithm based on agreement among soil sampling techniquesen_US
dc.typeArticleen_US

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