Matches in SemOpenAlex for { <https://semopenalex.org/work/W3083210749> ?p ?o ?g. }
Showing items 1 to 85 of
85
with 100 items per page.
- W3083210749 endingPage "236" @default.
- W3083210749 startingPage "227" @default.
- W3083210749 abstract "Nowadays, intelligent systems are used as innovative tools in different environmental issues. However, the prediction of short-term waste, unlike the long-term scale, is less developed due to more uncertainties and the difficulty in determining measurable independent parameters. In this study, two types of artificial neural networks (MLP and RBF) and two decision tree algorithms (CHAID and CART) have been used as effective tools for short-term forecasting of total waste production in coastal areas of Noor in Mazandaran Province, Iran. So that, average temperature, daily rainfall, sunny hours, maximum relative humidity and maximum wind speed were determined as the most important independent parameters, while the amount of waste produced in the coastal areas of Noor was considered as the dependent variable. Wastes from the coastal areas were gathered and their weights were analysed during 12 months from July 2017 through June 2018. Samplings were carried out twice a week, three weeks of a month and 12 months of a year, overall 72 times a year. The required meteorological data was gathered from the meteorological station in Noor. Then the sensitivity analysis was performed to check the independency of the major independent parameters. Thereafter, the mentioned machine learning approaches were applied to predict the short-term total waste production in IBM SPSS Modeler version 18 environment. In the applied models, 60% of data were used in training the model and the other 40% were used for model evaluation. The results indicated that the CHAID tree algorithm exhibits a better performance in predicting total solid waste production compared to CART, MLP and RBF models. The mean absolute error and the correlation coefficient (R) of CHAID algorithm was 0.067 and 0.828, respectively." @default.
- W3083210749 created "2020-09-11" @default.
- W3083210749 creator A5000652460 @default.
- W3083210749 creator A5011100636 @default.
- W3083210749 creator A5028861606 @default.
- W3083210749 creator A5053594092 @default.
- W3083210749 creator A5056367604 @default.
- W3083210749 date "2020-07-01" @default.
- W3083210749 modified "2023-09-26" @default.
- W3083210749 title "Coastal solid waste prediction by applying machine learning approaches (Case study: Noor, Mazandaran Province, Iran)" @default.
- W3083210749 doi "https://doi.org/10.22124/cjes.2020.4135" @default.
- W3083210749 hasPublicationYear "2020" @default.
- W3083210749 type Work @default.
- W3083210749 sameAs 3083210749 @default.
- W3083210749 citedByCount "1" @default.
- W3083210749 countsByYear W30832107492022 @default.
- W3083210749 crossrefType "journal-article" @default.
- W3083210749 hasAuthorship W3083210749A5000652460 @default.
- W3083210749 hasAuthorship W3083210749A5011100636 @default.
- W3083210749 hasAuthorship W3083210749A5028861606 @default.
- W3083210749 hasAuthorship W3083210749A5053594092 @default.
- W3083210749 hasAuthorship W3083210749A5056367604 @default.
- W3083210749 hasConcept C119857082 @default.
- W3083210749 hasConcept C127413603 @default.
- W3083210749 hasConcept C153294291 @default.
- W3083210749 hasConcept C158960510 @default.
- W3083210749 hasConcept C16023879 @default.
- W3083210749 hasConcept C161067210 @default.
- W3083210749 hasConcept C171250308 @default.
- W3083210749 hasConcept C192562407 @default.
- W3083210749 hasConcept C205649164 @default.
- W3083210749 hasConcept C39432304 @default.
- W3083210749 hasConcept C41008148 @default.
- W3083210749 hasConcept C50644808 @default.
- W3083210749 hasConcept C548081761 @default.
- W3083210749 hasConcept C70388272 @default.
- W3083210749 hasConcept C75779659 @default.
- W3083210749 hasConcept C84525736 @default.
- W3083210749 hasConceptScore W3083210749C119857082 @default.
- W3083210749 hasConceptScore W3083210749C127413603 @default.
- W3083210749 hasConceptScore W3083210749C153294291 @default.
- W3083210749 hasConceptScore W3083210749C158960510 @default.
- W3083210749 hasConceptScore W3083210749C16023879 @default.
- W3083210749 hasConceptScore W3083210749C161067210 @default.
- W3083210749 hasConceptScore W3083210749C171250308 @default.
- W3083210749 hasConceptScore W3083210749C192562407 @default.
- W3083210749 hasConceptScore W3083210749C205649164 @default.
- W3083210749 hasConceptScore W3083210749C39432304 @default.
- W3083210749 hasConceptScore W3083210749C41008148 @default.
- W3083210749 hasConceptScore W3083210749C50644808 @default.
- W3083210749 hasConceptScore W3083210749C548081761 @default.
- W3083210749 hasConceptScore W3083210749C70388272 @default.
- W3083210749 hasConceptScore W3083210749C75779659 @default.
- W3083210749 hasConceptScore W3083210749C84525736 @default.
- W3083210749 hasIssue "3" @default.
- W3083210749 hasLocation W30832107491 @default.
- W3083210749 hasOpenAccess W3083210749 @default.
- W3083210749 hasPrimaryLocation W30832107491 @default.
- W3083210749 hasRelatedWork W183870227 @default.
- W3083210749 hasRelatedWork W1984509278 @default.
- W3083210749 hasRelatedWork W2009821691 @default.
- W3083210749 hasRelatedWork W2011154736 @default.
- W3083210749 hasRelatedWork W2041628107 @default.
- W3083210749 hasRelatedWork W2165809139 @default.
- W3083210749 hasRelatedWork W2513834919 @default.
- W3083210749 hasRelatedWork W2550270861 @default.
- W3083210749 hasRelatedWork W2739001458 @default.
- W3083210749 hasRelatedWork W2741647354 @default.
- W3083210749 hasRelatedWork W2752575000 @default.
- W3083210749 hasRelatedWork W2888539845 @default.
- W3083210749 hasRelatedWork W2928368587 @default.
- W3083210749 hasRelatedWork W2947552276 @default.
- W3083210749 hasRelatedWork W2979774498 @default.
- W3083210749 hasRelatedWork W2993924946 @default.
- W3083210749 hasRelatedWork W3049425709 @default.
- W3083210749 hasRelatedWork W3080252818 @default.
- W3083210749 hasRelatedWork W3107650384 @default.
- W3083210749 hasRelatedWork W3128897223 @default.
- W3083210749 hasVolume "18" @default.
- W3083210749 isParatext "false" @default.
- W3083210749 isRetracted "false" @default.
- W3083210749 magId "3083210749" @default.
- W3083210749 workType "article" @default.