{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T09:06:07Z","timestamp":1781168767985,"version":"3.54.1"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T00:00:00Z","timestamp":1645747200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,7,25]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Knowing the level of quality from which the context is no longer valuable in a Context-Aware Data Mining (CADM) system is an important information. The main goal of this research is to study the variations of the predictions in case of different levels of noise and missing context data in practical scenarios for predicting soil moisture. The research has been performed on two locations from the Transylvanian Plain, Romania and two locations from Canada. The values predicted for the soil moisture were compared in mixed scenarios that vary the quantity of noise and missing context data. The studied behavior was performed using Deep Learning, Decision Tree and Gradient Boosted Tree machine learning algorithms. It has been shown that when using the air temperature as context for predicting soil moisture, variations of noise and missing data do not influence the results proportionally with the levels of noise and missing data applied. Also, Gradient Boosted Tree algorithm proves to be the best algorithm from the ones studied, to be considered when predicting soil moisture with the CADM approach.<\/jats:p>","DOI":"10.1093\/jigpal\/jzac038","type":"journal-article","created":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T20:23:43Z","timestamp":1644870223000},"page":"762-774","source":"Crossref","is-referenced-by-count":2,"title":["Influence of context availability and soundness in predicting soil moisture using the Context-Aware Data Mining approach"],"prefix":"10.1093","volume":"31","author":[{"given":"Anca","family":"Avram","sequence":"first","affiliation":[{"name":"Technical University of Cluj-Napoca, North University Center at Baia Mare , 430083 Baia Mare, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oliviu","family":"Matei","sequence":"additional","affiliation":[{"name":"Technical University of Cluj-Napoca, North University Center at Baia Mare , 430083 Baia Mare, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Camelia-M","family":"Pintea","sequence":"additional","affiliation":[{"name":"Technical University of Cluj-Napoca, North University Center at Baia Mare , 430083 Baia Mare, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Petrica C","family":"Pop","sequence":"additional","affiliation":[{"name":"Technical University of Cluj-Napoca, North University Center at Baia Mare , 430083 Baia Mare, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,2,25]]},"reference":[{"key":"2023072813242825400_ref1","volume-title":"Current and historical alberta weather station data viewer"},{"key":"2023072813242825400_ref2","doi-asserted-by":"crossref","first-page":"112301","DOI":"10.1016\/j.rse.2021.112301","article-title":"Generating surface soil moisture at 30 m spatial resolution using both data fusion and machine learning toward better water resources management at the field scale","volume":"255","author":"Abowarda","year":"2021","journal-title":"Remote Sensing of Environment"},{"key":"2023072813242825400_ref3","first-page":"31","article-title":"Performance analysis of collaborative data mining vs context aware data mining in a practical scenario for predicting air humidity","volume-title":"Computational Statistics and Mathematical Modeling Methods in Intelligent Systems: Proceedings of 3rd Computational Methods in Systems and Software 2019","author":"Anton","year":"2019"},{"key":"2023072813242825400_ref4","first-page":"199","article-title":"Context-aware data mining vs classical data mining: case study on predicting soil moisture","volume":"950","author":"Avram","year":"2019","journal-title":"14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019): Seville, Spain, May 13\u201315, 2019, Proceedings"},{"key":"2023072813242825400_ref5","doi-asserted-by":"crossref","first-page":"684","DOI":"10.3390\/math8050684","article-title":"Innovative platform for designing hybrid collaborative & context-aware data mining scenarios","volume":"8","author":"Avram","year":"2020","journal-title":"Mathematics"},{"key":"2023072813242825400_ref6","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1080\/01969722.2020.1798642","article-title":"Context quality impact in context-aware data mining for predicting soil moisture","volume":"51","author":"Avram","year":"2020","journal-title":"Cybernetics and Systems"},{"key":"2023072813242825400_ref7","first-page":"22","article-title":"How noisy and missing context influences predictions in a practical context-aware data mining system","volume-title":"15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)","author":"Avram","year":"2020"},{"key":"2023072813242825400_ref8","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1145\/2333112.2333119","article-title":"A survey of context data distribution for mobile ubiquitous systems","volume":"44","author":"Bellavista","year":"2012","journal-title":"ACM Computing Surveys"},{"key":"2023072813242825400_ref9","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s007790170019","article-title":"Understanding and using context","volume":"5","author":"Dey","year":"2001","journal-title":"Personal and Ubiquitous Computing"},{"key":"2023072813242825400_ref10","first-page":"502","article-title":"Spearman rank correlation coefficient","volume-title":"The Concise Encyclopedia of Statistics","author":"Dodge","year":"2008"},{"key":"2023072813242825400_ref11","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1256\/004316502320517344","article-title":"Climate change 2001: the scientific basis. 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