Matches in SemOpenAlex for { <https://semopenalex.org/work/W4321232612> ?p ?o ?g. }
- W4321232612 endingPage "3691" @default.
- W4321232612 startingPage "3691" @default.
- W4321232612 abstract "Owing to the rapid increase in construction and demolition (C&D) waste, the information of waste generation (WG) has been advantageously utilized as a strategy for C&D waste management. Recently, artificial intelligence (AI) has been strategically employed to obtain accurate WG information. Thus, this study aimed to manage demolition waste (DW) by combining three algorithms: artificial neural network (multilayer perceptron) (ANN-MLP), support vector regression (SVR), and random forest (RF) with an autoencoder (AE) to develop and test hybrid machine learning (ML) models. As a result of this study, AE technology significantly improved the performance of the ANN model. Especially, the performance of AE (25 features)–ANN model was superior to that of other non-hybrid and hybrid models. Compared to the non-hybrid ANN model, the performance of AE (25 features)–ANN model improved by 49%, 27%, 49%, and 22% in terms of the MAE, RMSE, R2, and R, respectively. The hybrid model using ANN and AE proposed in this study showed useful results to improve the performance of the DWGR ML model. Therefore, this method is considered a novel and advantageous approach for developing a DWGR ML model. Furthermore, it can be used to develop AI models for improving performance in various fields." @default.
- W4321232612 created "2023-02-18" @default.
- W4321232612 creator A5002825045 @default.
- W4321232612 creator A5043137954 @default.
- W4321232612 creator A5048304943 @default.
- W4321232612 date "2023-02-16" @default.
- W4321232612 modified "2023-10-17" @default.
- W4321232612 title "Performance Improvement of Machine Learning Model Using Autoencoder to Predict Demolition Waste Generation Rate" @default.
- W4321232612 cites W1967217858 @default.
- W4321232612 cites W1981816266 @default.
- W4321232612 cites W1989369784 @default.
- W4321232612 cites W1992914124 @default.
- W4321232612 cites W1996020380 @default.
- W4321232612 cites W2000548672 @default.
- W4321232612 cites W2020832745 @default.
- W4321232612 cites W2026527569 @default.
- W4321232612 cites W2029513327 @default.
- W4321232612 cites W2042079766 @default.
- W4321232612 cites W2049118732 @default.
- W4321232612 cites W2051827241 @default.
- W4321232612 cites W2069942641 @default.
- W4321232612 cites W2076722479 @default.
- W4321232612 cites W2088180337 @default.
- W4321232612 cites W2088758237 @default.
- W4321232612 cites W2097230627 @default.
- W4321232612 cites W2110983336 @default.
- W4321232612 cites W2118023920 @default.
- W4321232612 cites W2138258757 @default.
- W4321232612 cites W2140499889 @default.
- W4321232612 cites W2141692439 @default.
- W4321232612 cites W2153635508 @default.
- W4321232612 cites W2182833538 @default.
- W4321232612 cites W2292698124 @default.
- W4321232612 cites W2412393926 @default.
- W4321232612 cites W2523063366 @default.
- W4321232612 cites W2538533766 @default.
- W4321232612 cites W2587480881 @default.
- W4321232612 cites W2606466198 @default.
- W4321232612 cites W2611423883 @default.
- W4321232612 cites W2740722926 @default.
- W4321232612 cites W2751143443 @default.
- W4321232612 cites W2756291343 @default.
- W4321232612 cites W2774603473 @default.
- W4321232612 cites W2793604280 @default.
- W4321232612 cites W2800142469 @default.
- W4321232612 cites W2811019740 @default.
- W4321232612 cites W2889287984 @default.
- W4321232612 cites W2902098804 @default.
- W4321232612 cites W2909358758 @default.
- W4321232612 cites W2911794652 @default.
- W4321232612 cites W2911964244 @default.
- W4321232612 cites W2917171524 @default.
- W4321232612 cites W2971238524 @default.
- W4321232612 cites W3015508972 @default.
- W4321232612 cites W3020873385 @default.
- W4321232612 cites W3021052705 @default.
- W4321232612 cites W3023614603 @default.
- W4321232612 cites W3024048126 @default.
- W4321232612 cites W3027227991 @default.
- W4321232612 cites W3029459802 @default.
- W4321232612 cites W3084731547 @default.
- W4321232612 cites W3088963858 @default.
- W4321232612 cites W3120837814 @default.
- W4321232612 cites W3134563434 @default.
- W4321232612 cites W3136524022 @default.
- W4321232612 cites W3158947599 @default.
- W4321232612 cites W3167230339 @default.
- W4321232612 cites W3175861202 @default.
- W4321232612 cites W3181401814 @default.
- W4321232612 cites W3190325797 @default.
- W4321232612 cites W3191198889 @default.
- W4321232612 cites W3195665292 @default.
- W4321232612 cites W3200110661 @default.
- W4321232612 cites W4206566734 @default.
- W4321232612 cites W4229005816 @default.
- W4321232612 cites W4295775143 @default.
- W4321232612 cites W817616530 @default.
- W4321232612 doi "https://doi.org/10.3390/su15043691" @default.
- W4321232612 hasPublicationYear "2023" @default.
- W4321232612 type Work @default.
- W4321232612 citedByCount "2" @default.
- W4321232612 countsByYear W43212326122023 @default.
- W4321232612 crossrefType "journal-article" @default.
- W4321232612 hasAuthorship W4321232612A5002825045 @default.
- W4321232612 hasAuthorship W4321232612A5043137954 @default.
- W4321232612 hasAuthorship W4321232612A5048304943 @default.
- W4321232612 hasBestOaLocation W43212326121 @default.
- W4321232612 hasConcept C101738243 @default.
- W4321232612 hasConcept C105795698 @default.
- W4321232612 hasConcept C119857082 @default.
- W4321232612 hasConcept C12267149 @default.
- W4321232612 hasConcept C127413603 @default.
- W4321232612 hasConcept C139945424 @default.
- W4321232612 hasConcept C147176958 @default.
- W4321232612 hasConcept C154945302 @default.
- W4321232612 hasConcept C169258074 @default.
- W4321232612 hasConcept C179717631 @default.
- W4321232612 hasConcept C2778076428 @default.