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Experimental results show that the proposed SAE-BP model achieves a more stable and effective performance than the existing logistic regression (LR), support vector regression (SVR) and BP neural network with an average value of mean square error (MSE) = 1.926, mean absolute error (MAE) = 0.962 and coefficient of determination (R2) = 0.910. In addition, the effect of hidden structures and labeled training data ratios in SAE-BP is further explored. The SAE-BP model demonstrates the potential in high-dimensional and small hyperspectral datasets, representing a significant contribution to soil remote sensing.<\/jats:p>","DOI":"10.4018\/ijaci.2020070104","type":"journal-article","created":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T08:20:26Z","timestamp":1593591626000},"page":"66-79","source":"Crossref","is-referenced-by-count":4,"title":["Introducing a Hybrid Model SAE-BP for Regression Analysis of Soil Temperature With Hyperspectral Data"],"prefix":"10.4018","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1767-9166","authenticated-orcid":true,"given":"Miaomiao","family":"Ji","sequence":"first","affiliation":[{"name":"Northeast Agricultual University, Harbin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keke","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Engineering, Northeast Agricultural University, Harbin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4787-2549","authenticated-orcid":true,"given":"Qiufeng","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Science, Northeast Agricultural University, Harbin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJACI.2020070104-0","doi-asserted-by":"crossref","unstructured":"Baghbaderani, R. 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