IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i4p3691-d1071356.html
   My bibliography  Save this article

Performance Improvement of Machine Learning Model Using Autoencoder to Predict Demolition Waste Generation Rate

Author

Listed:
  • Gi-Wook Cha

    (School of Science and Technology Acceleration Engineering, Kyungpook National University, Daegu 41566, Republic of Korea)

  • Won-Hwa Hong

    (School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu 41566, Republic of Korea)

  • Young-Chan Kim

    (Division of Smart Safety Engineering, Dongguk University Wise Campus, 123 Dongdae-ro, Gyeongju 38066, Republic of Korea)

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, R 2 , 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.

Suggested Citation

  • Gi-Wook Cha & Won-Hwa Hong & Young-Chan Kim, 2023. "Performance Improvement of Machine Learning Model Using Autoencoder to Predict Demolition Waste Generation Rate," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3691-:d:1071356
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/4/3691/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/4/3691/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gi-Wook Cha & Hyeun-Jun Moon & Young-Chan Kim, 2021. "Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables," IJERPH, MDPI, vol. 18(16), pages 1-16, August.
    2. Márquez, Ma. Ysabel & Ojeda, Sara & Hidalgo, Hugo, 2008. "Identification of behavior patterns in household solid waste generation in Mexicali's city: Study case," Resources, Conservation & Recycling, Elsevier, vol. 52(11), pages 1299-1306.
    3. Gi-Wook Cha & Hyeun Jun Moon & Young-Min Kim & Won-Hwa Hong & Jung-Ha Hwang & Won-Jun Park & Young-Chan Kim, 2020. "Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets," IJERPH, MDPI, vol. 17(19), pages 1-15, September.
    4. Maria Triassi & Rossella Alfano & Maddalena Illario & Antonio Nardone & Oreste Caporale & Paolo Montuori, 2015. "Environmental Pollution from Illegal Waste Disposal and Health Effects: A Review on the “Triangle of Death”," IJERPH, MDPI, vol. 12(2), pages 1-21, January.
    5. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yingcui Du & Feng Sun & Fangtong Jiao & Benxing Liu & Xiaoqing Wang & Pengsheng Zhao, 2023. "The Identification of Intersection Entrance Accidents Based on Autoencoder," Sustainability, MDPI, vol. 15(11), pages 1-17, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gi-Wook Cha & Se-Hyu Choi & Won-Hwa Hong & Choon-Wook Park, 2022. "Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas," IJERPH, MDPI, vol. 20(1), pages 1-17, December.
    2. Gi-Wook Cha & Won-Hwa Hong & Se-Hyu Choi & Young-Chan Kim, 2023. "Developing an Optimal Ensemble Model to Estimate Building Demolition Waste Generation Rate," Sustainability, MDPI, vol. 15(13), pages 1-20, June.
    3. Gi-Wook Cha & Se-Hyu Choi & Won-Hwa Hong & Choon-Wook Park, 2023. "Developing a Prediction Model of Demolition-Waste Generation-Rate via Principal Component Analysis," IJERPH, MDPI, vol. 20(4), pages 1-15, February.
    4. Maurizio Bifulco, 2015. "Comments on Triassi et al. Environmental Pollution from Illegal Waste Disposal and Health Effects: A Review on the “Triangle of Death”. Int. J. Environ. Res. Public Health 2015, 12 , 1216–1236," IJERPH, MDPI, vol. 12(3), pages 1-2, March.
    5. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    6. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    7. Vivianni Palmeira Wanderley & Fernando Luiz Affonso Fonseca & André Vala Quiaios & José Nuno Domingues & Susana Paixão & João Figueiredo & Ana Ferreira & Cleonice De Almeida Pinto & Odair Ramos Da Sil, 2017. "Socio-Environmental and Hematological Profile of Landfill Residents (São Jorge Landfill–Sao Paulo, Brazil)," IJERPH, MDPI, vol. 14(1), pages 1-12, January.
    8. Yunlong Han & Conghui Li & Linfeng Zheng & Gang Lei & Li Li, 2023. "Remaining Useful Life Prediction of Lithium-Ion Batteries by Using a Denoising Transformer-Based Neural Network," Energies, MDPI, vol. 16(17), pages 1-16, August.
    9. Ma, Tao & Zhang, Yijie & Gu, Wenbo & Xiao, Gang & Yang, Hongxing & Wang, Shuxiao, 2022. "Strategy comparison and techno-economic evaluation of a grid-connected photovoltaic-battery system," Renewable Energy, Elsevier, vol. 197(C), pages 1049-1060.
    10. Fan Yu & Lei Wang & Qiaoyong Jiang & Qunmin Yan & Shi Qiao, 2022. "Self-Attention-Based Short-Term Load Forecasting Considering Demand-Side Management," Energies, MDPI, vol. 15(12), pages 1-19, June.
    11. Abdurahman Alrobaie & Moncef Krarti, 2022. "A Review of Data-Driven Approaches for Measurement and Verification Analysis of Building Energy Retrofits," Energies, MDPI, vol. 15(21), pages 1-30, October.
    12. Christian Nnaemeka Egwim & Hafiz Alaka & Eren Demir & Habeeb Balogun & Razak Olu-Ajayi & Ismail Sulaimon & Godoyon Wusu & Wasiu Yusuf & Adegoke A. Muideen, 2023. "Artificial Intelligence in the Construction Industry: A Systematic Review of the Entire Construction Value Chain Lifecycle," Energies, MDPI, vol. 17(1), pages 1-21, December.
    13. Andreea Valeria Vesa & Tudor Cioara & Ionut Anghel & Marcel Antal & Claudia Pop & Bogdan Iancu & Ioan Salomie & Vasile Teodor Dadarlat, 2020. "Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs," Sustainability, MDPI, vol. 12(4), pages 1-23, February.
    14. Sunil Kumar Mohapatra & Sushruta Mishra & Hrudaya Kumar Tripathy & Akash Kumar Bhoi & Paolo Barsocchi, 2021. "A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches," Energies, MDPI, vol. 14(13), pages 1-28, June.
    15. Wang, Haoxuan & Chen, Huaian & Wang, Ben & Jin, Yi & Li, Guiqiang & Kan, Yan, 2022. "High-efficiency low-power microdefect detection in photovoltaic cells via a field programmable gate array-accelerated dual-flow network," Applied Energy, Elsevier, vol. 318(C).
    16. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    17. Cameron Francis Assadian & Francis Assadian, 2023. "Data-Driven Modeling of Appliance Energy Usage," Energies, MDPI, vol. 16(22), pages 1-12, November.
    18. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    19. Li, Guannan & Li, Fan & Ahmad, Tanveer & Liu, Jiangyan & Li, Tao & Fang, Xi & Wu, Yubei, 2022. "Performance evaluation of sequence-to-sequence-Attention model for short-term multi-step ahead building energy predictions," Energy, Elsevier, vol. 259(C).
    20. Jürgen Dunkel & David Dominguez & Óscar G. Borzdynski & Ángel Sánchez, 2022. "Solid Waste Analysis Using Open-Access Socio-Economic Data," Sustainability, MDPI, vol. 14(3), pages 1-24, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3691-:d:1071356. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.