Matches in SemOpenAlex for { <https://semopenalex.org/work/W2040881457> ?p ?o ?g. }
Showing items 1 to 100 of
100
with 100 items per page.
- W2040881457 endingPage "163" @default.
- W2040881457 startingPage "150" @default.
- W2040881457 abstract "We present a general methodology for developing environmental emergency decision support systems (EEDSS) based on an Artificial Neural Network (ANN). We highlight the method for developing the system using an illustrative example of an unexpected atmospheric accident with an ANN prototype system for a district in Shanghai. The network architecture of the ANN is introduced. Then the development process and key technologies are addressed. The procedures for matching the environmental emergency decision support characteristics are as follows: (1) digitization (coding) of case information and emergency measures, in which the information of cases are divided into the input attributes and decision-making information, and standardized and digitized through the Feature Evaluation (FE) method and the Intensity Hierarchical (IH) method, respectively; (2) construction of environmental emergency ANN, in which Gradient Descent with Momentum and Adaptive Learning Rate (GDMALR) method (traingdx function), a modified back-propagation algorithm, is employed to do training; and (3) translation (decoding) of decision-making information, in which output data of ANN is interpreted into practical contingency measures with Translation Based on Conventional Import Ratios (TBCIR) method. The training features, time, errors, accuracy, and input attribute weights of the prototype system are analyzed. The usage of the prototype system is demonstrated through a hypothetical case. This article encounters the challenge of ANN’s own lack of training samples. We discuss to the concept of integrating Case-Based Reasoning (CBR), Genetic Algorithm (GA), and ANN to overcome this difficulty and form a technology system for generating useful decision support information for environmental emergency response." @default.
- W2040881457 created "2016-06-24" @default.
- W2040881457 creator A5022054190 @default.
- W2040881457 creator A5033821306 @default.
- W2040881457 creator A5065579510 @default.
- W2040881457 creator A5088855219 @default.
- W2040881457 date "2012-01-01" @default.
- W2040881457 modified "2023-09-27" @default.
- W2040881457 title "Environmental emergency decision support system based on Artificial Neural Network" @default.
- W2040881457 cites W1562601998 @default.
- W2040881457 cites W1607790281 @default.
- W2040881457 cites W1972797209 @default.
- W2040881457 cites W1975150609 @default.
- W2040881457 cites W1985401421 @default.
- W2040881457 cites W2007765270 @default.
- W2040881457 cites W2009774687 @default.
- W2040881457 cites W2013426315 @default.
- W2040881457 cites W2031965811 @default.
- W2040881457 cites W2032122467 @default.
- W2040881457 cites W2036535026 @default.
- W2040881457 cites W2041949441 @default.
- W2040881457 cites W2050681470 @default.
- W2040881457 cites W2055383075 @default.
- W2040881457 cites W2066411930 @default.
- W2040881457 cites W2077011856 @default.
- W2040881457 cites W2079933213 @default.
- W2040881457 cites W2080598448 @default.
- W2040881457 cites W2092064326 @default.
- W2040881457 cites W2133295230 @default.
- W2040881457 cites W2139437202 @default.
- W2040881457 doi "https://doi.org/10.1016/j.ssci.2011.07.014" @default.
- W2040881457 hasPublicationYear "2012" @default.
- W2040881457 type Work @default.
- W2040881457 sameAs 2040881457 @default.
- W2040881457 citedByCount "27" @default.
- W2040881457 countsByYear W20408814572012 @default.
- W2040881457 countsByYear W20408814572013 @default.
- W2040881457 countsByYear W20408814572014 @default.
- W2040881457 countsByYear W20408814572015 @default.
- W2040881457 countsByYear W20408814572016 @default.
- W2040881457 countsByYear W20408814572017 @default.
- W2040881457 countsByYear W20408814572018 @default.
- W2040881457 countsByYear W20408814572019 @default.
- W2040881457 countsByYear W20408814572020 @default.
- W2040881457 countsByYear W20408814572022 @default.
- W2040881457 countsByYear W20408814572023 @default.
- W2040881457 crossrefType "journal-article" @default.
- W2040881457 hasAuthorship W2040881457A5022054190 @default.
- W2040881457 hasAuthorship W2040881457A5033821306 @default.
- W2040881457 hasAuthorship W2040881457A5065579510 @default.
- W2040881457 hasAuthorship W2040881457A5088855219 @default.
- W2040881457 hasConcept C105795698 @default.
- W2040881457 hasConcept C107327155 @default.
- W2040881457 hasConcept C119857082 @default.
- W2040881457 hasConcept C124101348 @default.
- W2040881457 hasConcept C154945302 @default.
- W2040881457 hasConcept C179518139 @default.
- W2040881457 hasConcept C20162079 @default.
- W2040881457 hasConcept C26517878 @default.
- W2040881457 hasConcept C2779308522 @default.
- W2040881457 hasConcept C31972630 @default.
- W2040881457 hasConcept C33923547 @default.
- W2040881457 hasConcept C38652104 @default.
- W2040881457 hasConcept C41008148 @default.
- W2040881457 hasConcept C50644808 @default.
- W2040881457 hasConceptScore W2040881457C105795698 @default.
- W2040881457 hasConceptScore W2040881457C107327155 @default.
- W2040881457 hasConceptScore W2040881457C119857082 @default.
- W2040881457 hasConceptScore W2040881457C124101348 @default.
- W2040881457 hasConceptScore W2040881457C154945302 @default.
- W2040881457 hasConceptScore W2040881457C179518139 @default.
- W2040881457 hasConceptScore W2040881457C20162079 @default.
- W2040881457 hasConceptScore W2040881457C26517878 @default.
- W2040881457 hasConceptScore W2040881457C2779308522 @default.
- W2040881457 hasConceptScore W2040881457C31972630 @default.
- W2040881457 hasConceptScore W2040881457C33923547 @default.
- W2040881457 hasConceptScore W2040881457C38652104 @default.
- W2040881457 hasConceptScore W2040881457C41008148 @default.
- W2040881457 hasConceptScore W2040881457C50644808 @default.
- W2040881457 hasIssue "1" @default.
- W2040881457 hasLocation W20408814571 @default.
- W2040881457 hasOpenAccess W2040881457 @default.
- W2040881457 hasPrimaryLocation W20408814571 @default.
- W2040881457 hasRelatedWork W2329452785 @default.
- W2040881457 hasRelatedWork W2356380379 @default.
- W2040881457 hasRelatedWork W2961085424 @default.
- W2040881457 hasRelatedWork W3046775127 @default.
- W2040881457 hasRelatedWork W3170094116 @default.
- W2040881457 hasRelatedWork W4285260836 @default.
- W2040881457 hasRelatedWork W4286629047 @default.
- W2040881457 hasRelatedWork W4306321456 @default.
- W2040881457 hasRelatedWork W4306674287 @default.
- W2040881457 hasRelatedWork W4224009465 @default.
- W2040881457 hasVolume "50" @default.
- W2040881457 isParatext "false" @default.
- W2040881457 isRetracted "false" @default.
- W2040881457 magId "2040881457" @default.
- W2040881457 workType "article" @default.