FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2782072844> ?p ?o ?g. }

• W2782072844 endingPage "41" @default.
• W2782072844 startingPage "29" @default.
• W2782072844 abstract "In order to limit pollution risk and develop proper remediation strategies, soil quality has to be controlled by rapid and sustainable monitoring measures. Visible and near-infrared reflectance spectroscopy (VisNIR) is an attractive surrogate to time-consuming and costly classical soil assessment protocols. It highly depends on selecting appropriate data mining methods for regression analysis. In this study, performance of a state of the art learning algorithm called extreme learning machine (ELM), was evaluated through comparing with the other calibration methods proposed in the literature for predicting lead (Pb) and Zinc (Zn) concentrations. Solid samples collected from a mine waste dump (n = 120) were scanned using a Fieldspec3 portable spectroradiometer with a measurement range of (350–2500 nm) in a laboratory. Transformation of the reflectance spectra to absorbance was followed by three pre-processing scenarios including Savitzky-Golay smoothing (SG), first derivative (FD) and second derivative (SD). Partial Least Square Regression (PLSR), Support Vector Machine (SVM) and neural networks with two learning algorithms models (back propagation and extreme learning machine), were calibrated on spectral features selected by genetic algorithm, and then applied to predict soil metal concentrations. The best prediction accuracy was obtained by FD-ELM method with R2p, RMSEp, concordance correlation coefficient and RPD values of 0.93, 63.01, 0.98 and 5.92 for Pb and 0.87, 167.90, 0.91 and 5.62 for Zn, respectively. Study of the prediction mechanism proved that element sorption by spectrally active Fe-oxide and clay contents of the soil was the major mechanism by which the spectrally featureless Pb and Zn ions can be predicted. The spatial patterns of predicted toxic elements showed that FD-ELM had the most similarity with those maps obtained by interpolating measured values. Over all, it is concluded that reflectance spectroscopy combined with the ELM algorithm is a rapid, inexpensive and accurate tool for indirect evaluation of Pb and Zn and mapping their spatial distribution in dumpsite soils of Sarcheshmeh copper mine." @default.
• W2782072844 created "2018-01-12" @default.
• W2782072844 creator A5032061318 @default.
• W2782072844 creator A5047002961 @default.
• W2782072844 creator A5057141894 @default.
• W2782072844 creator A5071535336 @default.
• W2782072844 date "2018-05-01" @default.
• W2782072844 modified "2023-10-14" @default.
• W2782072844 title "Monitoring soil lead and zinc contents via combination of spectroscopy with extreme learning machine and other data mining methods" @default.
• W2782072844 cites W1148445081 @default.
• W2782072844 cites W1970205353 @default.
• W2782072844 cites W1971151819 @default.
• W2782072844 cites W1973273412 @default.
• W2782072844 cites W1976355094 @default.
• W2782072844 cites W1984466055 @default.
• W2782072844 cites W1985931804 @default.
• W2782072844 cites W1986109834 @default.
• W2782072844 cites W1986530877 @default.
• W2782072844 cites W1999048942 @default.
• W2782072844 cites W2001569762 @default.
• W2782072844 cites W2002231918 @default.
• W2782072844 cites W2009118062 @default.
• W2782072844 cites W2010212234 @default.
• W2782072844 cites W2013290860 @default.
• W2782072844 cites W2016386623 @default.
• W2782072844 cites W2019362960 @default.
• W2782072844 cites W2021754455 @default.
• W2782072844 cites W2023853561 @default.
• W2782072844 cites W2025857348 @default.
• W2782072844 cites W2039246958 @default.
• W2782072844 cites W2039296727 @default.
• W2782072844 cites W2040604977 @default.
• W2782072844 cites W2051703974 @default.
• W2782072844 cites W2056721262 @default.
• W2782072844 cites W2058096965 @default.
• W2782072844 cites W2061407528 @default.
• W2782072844 cites W2071157591 @default.
• W2782072844 cites W2077238186 @default.
• W2782072844 cites W2080162823 @default.
• W2782072844 cites W2080292938 @default.
• W2782072844 cites W2090422954 @default.
• W2782072844 cites W2090517596 @default.
• W2782072844 cites W2098153202 @default.
• W2782072844 cites W2100812469 @default.
• W2782072844 cites W2104487864 @default.
• W2782072844 cites W2111072639 @default.
• W2782072844 cites W2116273223 @default.
• W2782072844 cites W2174968341 @default.
• W2782072844 cites W2225651437 @default.
• W2782072844 cites W2277214347 @default.
• W2782072844 cites W2313339984 @default.
• W2782072844 cites W2341909949 @default.
• W2782072844 cites W2464502523 @default.
• W2782072844 cites W2513890697 @default.
• W2782072844 cites W2551518784 @default.
• W2782072844 cites W2555930296 @default.
• W2782072844 cites W2558714883 @default.
• W2782072844 cites W2563958088 @default.
• W2782072844 cites W2587369937 @default.
• W2782072844 cites W2725541299 @default.
• W2782072844 cites W906019740 @default.
• W2782072844 doi "https://doi.org/10.1016/j.geoderma.2017.12.025" @default.
• W2782072844 hasPublicationYear "2018" @default.
• W2782072844 type Work @default.
• W2782072844 sameAs 2782072844 @default.
• W2782072844 citedByCount "98" @default.
• W2782072844 countsByYear W27820728442018 @default.
• W2782072844 countsByYear W27820728442019 @default.
• W2782072844 countsByYear W27820728442020 @default.
• W2782072844 countsByYear W27820728442021 @default.
• W2782072844 countsByYear W27820728442022 @default.
• W2782072844 countsByYear W27820728442023 @default.
• W2782072844 crossrefType "journal-article" @default.
• W2782072844 hasAuthorship W2782072844A5032061318 @default.
• W2782072844 hasAuthorship W2782072844A5047002961 @default.
• W2782072844 hasAuthorship W2782072844A5057141894 @default.
• W2782072844 hasAuthorship W2782072844A5071535336 @default.
• W2782072844 hasConcept C105795698 @default.
• W2782072844 hasConcept C11413529 @default.
• W2782072844 hasConcept C119857082 @default.
• W2782072844 hasConcept C12267149 @default.
• W2782072844 hasConcept C139945424 @default.
• W2782072844 hasConcept C154945302 @default.
• W2782072844 hasConcept C22354355 @default.
• W2782072844 hasConcept C2780150128 @default.
• W2782072844 hasConcept C33923547 @default.
• W2782072844 hasConcept C3770464 @default.
• W2782072844 hasConcept C39432304 @default.
• W2782072844 hasConcept C41008148 @default.
• W2782072844 hasConcept C50644808 @default.
• W2782072844 hasConceptScore W2782072844C105795698 @default.
• W2782072844 hasConceptScore W2782072844C11413529 @default.
• W2782072844 hasConceptScore W2782072844C119857082 @default.
• W2782072844 hasConceptScore W2782072844C12267149 @default.
• W2782072844 hasConceptScore W2782072844C139945424 @default.
• W2782072844 hasConceptScore W2782072844C154945302 @default.
• W2782072844 hasConceptScore W2782072844C22354355 @default.
• W2782072844 hasConceptScore W2782072844C2780150128 @default.

• next