FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2073284025> ?p ?o ?g. }

• W2073284025 endingPage "46" @default.
• W2073284025 startingPage "29" @default.
• W2073284025 abstract "Abstract This paper illustrates the application of artificial neural networks (ANN) for prediction of pesticide occurrence in rural domestic wells from the available limited information. Among the three ANN models (a feed-forward back propagation [BP], a radial basis function [RBF] and an adaptive neural network-based fuzzy inference system [ANFIS]) employed for this investigation, the BP neural network was found to be superior to RBF and ANFIS type networks for the detection of pesticide occurrences in wells. For improved model prediction efficiency, optimization of network structure (e.g., number of hidden layers and number of nodes in each hidden layer) and spread (the width of the radial basis function) are important for BP and RBF type of network, respectively. A four layer BP network with a 3:2 neurons ratio of the first hidden layer to the second hidden layer produced better prediction performance efficiencies in terms of the pesticide detection efficiency ( E f ), the root mean square error (RMSE), and the correlation coefficient ( R ) and the overall E f of the BP neural network was found greater than 85%. Sensitivity analysis was performed to measure the relative importance of one input parameter over the other in pesticide occurrence in wells. It was shown in terms of the prediction efficiencies ( E f , RMSE, and R ) of a four-layer BP neural network that the time of sample collection (TSC; month of the year), the depths of wells, and pesticide travel times (PTT) were more important parameters in the prediction of the pesticide occurrences in rural domestic wells. This means that the wells having shallow ground water table are more vulnerable to pesticide occurrence." @default.
• W2073284025 created "2016-06-24" @default.
• W2073284025 creator A5028115029 @default.
• W2073284025 creator A5038063744 @default.
• W2073284025 creator A5085079624 @default.
• W2073284025 date "2005-04-01" @default.
• W2073284025 modified "2023-10-01" @default.
• W2073284025 title "Pesticide prediction in ground water in North Carolina domestic wells using artificial neural networks" @default.
• W2073284025 cites W1580822624 @default.
• W2073284025 cites W1912463333 @default.
• W2073284025 cites W1964385596 @default.
• W2073284025 cites W1965040090 @default.
• W2073284025 cites W1969000438 @default.
• W2073284025 cites W1974313232 @default.
• W2073284025 cites W1975640350 @default.
• W2073284025 cites W1977464589 @default.
• W2073284025 cites W1986760892 @default.
• W2073284025 cites W1995930498 @default.
• W2073284025 cites W1998442441 @default.
• W2073284025 cites W2010555608 @default.
• W2073284025 cites W2018790612 @default.
• W2073284025 cites W2023567628 @default.
• W2073284025 cites W2025301416 @default.
• W2073284025 cites W2031292142 @default.
• W2073284025 cites W2033098857 @default.
• W2073284025 cites W2037460094 @default.
• W2073284025 cites W2043364632 @default.
• W2073284025 cites W2043398425 @default.
• W2073284025 cites W2048509079 @default.
• W2073284025 cites W2054639185 @default.
• W2073284025 cites W2056231849 @default.
• W2073284025 cites W2057788866 @default.
• W2073284025 cites W2058952413 @default.
• W2073284025 cites W2061830556 @default.
• W2073284025 cites W2064820716 @default.
• W2073284025 cites W2068491255 @default.
• W2073284025 cites W2071486925 @default.
• W2073284025 cites W2089497458 @default.
• W2073284025 cites W2090374427 @default.
• W2073284025 cites W2131048658 @default.
• W2073284025 cites W2141212940 @default.
• W2073284025 cites W2165758113 @default.
• W2073284025 cites W2171836956 @default.
• W2073284025 cites W2326531020 @default.
• W2073284025 cites W3018770027 @default.
• W2073284025 cites W349852107 @default.
• W2073284025 doi "https://doi.org/10.1016/j.ecolmodel.2004.07.021" @default.
• W2073284025 hasPublicationYear "2005" @default.
• W2073284025 type Work @default.
• W2073284025 sameAs 2073284025 @default.
• W2073284025 citedByCount "86" @default.
• W2073284025 countsByYear W20732840252012 @default.
• W2073284025 countsByYear W20732840252013 @default.
• W2073284025 countsByYear W20732840252014 @default.
• W2073284025 countsByYear W20732840252015 @default.
• W2073284025 countsByYear W20732840252016 @default.
• W2073284025 countsByYear W20732840252017 @default.
• W2073284025 countsByYear W20732840252018 @default.
• W2073284025 countsByYear W20732840252019 @default.
• W2073284025 countsByYear W20732840252020 @default.
• W2073284025 countsByYear W20732840252021 @default.
• W2073284025 countsByYear W20732840252022 @default.
• W2073284025 countsByYear W20732840252023 @default.
• W2073284025 crossrefType "journal-article" @default.
• W2073284025 hasAuthorship W2073284025A5028115029 @default.
• W2073284025 hasAuthorship W2073284025A5038063744 @default.
• W2073284025 hasAuthorship W2073284025A5085079624 @default.
• W2073284025 hasConcept C127313418 @default.
• W2073284025 hasConcept C154945302 @default.
• W2073284025 hasConcept C187320778 @default.
• W2073284025 hasConcept C18903297 @default.
• W2073284025 hasConcept C39432304 @default.
• W2073284025 hasConcept C41008148 @default.
• W2073284025 hasConcept C50644808 @default.
• W2073284025 hasConcept C76177295 @default.
• W2073284025 hasConcept C76886044 @default.
• W2073284025 hasConcept C86803240 @default.
• W2073284025 hasConceptScore W2073284025C127313418 @default.
• W2073284025 hasConceptScore W2073284025C154945302 @default.
• W2073284025 hasConceptScore W2073284025C187320778 @default.
• W2073284025 hasConceptScore W2073284025C18903297 @default.
• W2073284025 hasConceptScore W2073284025C39432304 @default.
• W2073284025 hasConceptScore W2073284025C41008148 @default.
• W2073284025 hasConceptScore W2073284025C50644808 @default.
• W2073284025 hasConceptScore W2073284025C76177295 @default.
• W2073284025 hasConceptScore W2073284025C76886044 @default.
• W2073284025 hasConceptScore W2073284025C86803240 @default.
• W2073284025 hasIssue "1" @default.
• W2073284025 hasLocation W20732840251 @default.
• W2073284025 hasOpenAccess W2073284025 @default.
• W2073284025 hasPrimaryLocation W20732840251 @default.
• W2073284025 hasRelatedWork W1484539862 @default.
• W2073284025 hasRelatedWork W157324318 @default.
• W2073284025 hasRelatedWork W2361555604 @default.
• W2073284025 hasRelatedWork W2377674743 @default.
• W2073284025 hasRelatedWork W2389139058 @default.
• W2073284025 hasRelatedWork W2564776538 @default.
• W2073284025 hasRelatedWork W2783783862 @default.

• next