Matches in SemOpenAlex for { <https://semopenalex.org/work/W2047546607> ?p ?o ?g. }
Showing items 1 to 94 of
94
with 100 items per page.
- W2047546607 endingPage "51" @default.
- W2047546607 startingPage "43" @default.
- W2047546607 abstract "Hydrologists and engineers need methods to disaggregate hourly rainfall data into subhourly increments for many hydrologic and hydraulic engineering applications. In the present engineering environment where time efficiency and cost effectiveness are paramount characteristics of engineering tools, disaggregation techniques must be practical and accurate. One particularly attractive technique for disaggregating long-term hourly rainfall records into subhourly increments involves the use of artificial neural networks (ANNs). A past investigation of ANN rainfall disaggregation models indicated that although ANNs can be applied effectively there are several considerations concerning the characteristics of the ANN model and the training methods employed. The research presented in this paper evaluated the influence on performance of several ANN model characteristics and training issues including data standardization, geographic location of training data, quantity of training data, number of training iterations, and the number of hidden neurons in the ANN. Results from this study suggest that data from rainfall-gauging stations within several hundred kilometers of the station to be disaggregated are adequate for training the ANN rainfall disaggregation model. Further, we found the number of training iterations, the limits of data standardization, the number of training data sets, and the number of hidden neurons in the ANN to exhibit varying degrees of influence over the ANN model performance." @default.
- W2047546607 created "2016-06-24" @default.
- W2047546607 creator A5020212379 @default.
- W2047546607 creator A5025930779 @default.
- W2047546607 creator A5031051561 @default.
- W2047546607 creator A5073565107 @default.
- W2047546607 date "2001-01-01" @default.
- W2047546607 modified "2023-09-27" @default.
- W2047546607 title "Training Artificial Neural Networks to Perform Rainfall Disaggregation" @default.
- W2047546607 cites W1968446526 @default.
- W2047546607 cites W1975056818 @default.
- W2047546607 cites W1979734969 @default.
- W2047546607 cites W1980332207 @default.
- W2047546607 cites W1986002268 @default.
- W2047546607 cites W1997052296 @default.
- W2047546607 cites W2005374071 @default.
- W2047546607 cites W2011412119 @default.
- W2047546607 cites W2018790612 @default.
- W2047546607 cites W2028136229 @default.
- W2047546607 cites W2058506198 @default.
- W2047546607 cites W2073860337 @default.
- W2047546607 cites W2086950433 @default.
- W2047546607 cites W2109716677 @default.
- W2047546607 cites W2123481634 @default.
- W2047546607 cites W2133721696 @default.
- W2047546607 cites W4238893454 @default.
- W2047546607 doi "https://doi.org/10.1061/(asce)1084-0699(2001)6:1(43)" @default.
- W2047546607 hasPublicationYear "2001" @default.
- W2047546607 type Work @default.
- W2047546607 sameAs 2047546607 @default.
- W2047546607 citedByCount "43" @default.
- W2047546607 countsByYear W20475466072012 @default.
- W2047546607 countsByYear W20475466072013 @default.
- W2047546607 countsByYear W20475466072014 @default.
- W2047546607 countsByYear W20475466072015 @default.
- W2047546607 countsByYear W20475466072016 @default.
- W2047546607 countsByYear W20475466072017 @default.
- W2047546607 countsByYear W20475466072018 @default.
- W2047546607 countsByYear W20475466072019 @default.
- W2047546607 countsByYear W20475466072020 @default.
- W2047546607 countsByYear W20475466072021 @default.
- W2047546607 countsByYear W20475466072022 @default.
- W2047546607 crossrefType "journal-article" @default.
- W2047546607 hasAuthorship W2047546607A5020212379 @default.
- W2047546607 hasAuthorship W2047546607A5025930779 @default.
- W2047546607 hasAuthorship W2047546607A5031051561 @default.
- W2047546607 hasAuthorship W2047546607A5073565107 @default.
- W2047546607 hasConcept C111919701 @default.
- W2047546607 hasConcept C119857082 @default.
- W2047546607 hasConcept C121332964 @default.
- W2047546607 hasConcept C124101348 @default.
- W2047546607 hasConcept C153294291 @default.
- W2047546607 hasConcept C154945302 @default.
- W2047546607 hasConcept C188087704 @default.
- W2047546607 hasConcept C2777211547 @default.
- W2047546607 hasConcept C41008148 @default.
- W2047546607 hasConcept C50644808 @default.
- W2047546607 hasConcept C51632099 @default.
- W2047546607 hasConcept C7879346 @default.
- W2047546607 hasConcept C97355855 @default.
- W2047546607 hasConceptScore W2047546607C111919701 @default.
- W2047546607 hasConceptScore W2047546607C119857082 @default.
- W2047546607 hasConceptScore W2047546607C121332964 @default.
- W2047546607 hasConceptScore W2047546607C124101348 @default.
- W2047546607 hasConceptScore W2047546607C153294291 @default.
- W2047546607 hasConceptScore W2047546607C154945302 @default.
- W2047546607 hasConceptScore W2047546607C188087704 @default.
- W2047546607 hasConceptScore W2047546607C2777211547 @default.
- W2047546607 hasConceptScore W2047546607C41008148 @default.
- W2047546607 hasConceptScore W2047546607C50644808 @default.
- W2047546607 hasConceptScore W2047546607C51632099 @default.
- W2047546607 hasConceptScore W2047546607C7879346 @default.
- W2047546607 hasConceptScore W2047546607C97355855 @default.
- W2047546607 hasIssue "1" @default.
- W2047546607 hasLocation W20475466071 @default.
- W2047546607 hasOpenAccess W2047546607 @default.
- W2047546607 hasPrimaryLocation W20475466071 @default.
- W2047546607 hasRelatedWork W2378979135 @default.
- W2047546607 hasRelatedWork W2567861069 @default.
- W2047546607 hasRelatedWork W2961085424 @default.
- W2047546607 hasRelatedWork W3046775127 @default.
- W2047546607 hasRelatedWork W3170094116 @default.
- W2047546607 hasRelatedWork W4285260836 @default.
- W2047546607 hasRelatedWork W4286629047 @default.
- W2047546607 hasRelatedWork W4306321456 @default.
- W2047546607 hasRelatedWork W4306674287 @default.
- W2047546607 hasRelatedWork W4224009465 @default.
- W2047546607 hasVolume "6" @default.
- W2047546607 isParatext "false" @default.
- W2047546607 isRetracted "false" @default.
- W2047546607 magId "2047546607" @default.
- W2047546607 workType "article" @default.