Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106878
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | en_US |
dc.creator | Yang, LH | en_US |
dc.creator | Wang, S | en_US |
dc.creator | Ye, FF | en_US |
dc.creator | Liu, J | en_US |
dc.creator | Wang, YM | en_US |
dc.creator | Hu, H | en_US |
dc.date.accessioned | 2024-06-07T00:58:35Z | - |
dc.date.available | 2024-06-07T00:58:35Z | - |
dc.identifier.issn | 0959-6526 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/106878 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier BV | en_US |
dc.rights | © 2020 Elsevier Ltd. All rights reserved. | en_US |
dc.rights | © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. | en_US |
dc.rights | The following publication Yang, L. H., Wang, S., Ye, F. F., Liu, J., Wang, Y. M., & Hu, H. (2021). Environmental investment prediction using extended belief rule-based system and evidential reasoning rule. Journal of Cleaner Production, 289, 125661 is available at https://doi.org/10.1016/j.jclepro.2020.125661. | en_US |
dc.subject | Ensemble model | en_US |
dc.subject | Evidential reasoning rule | en_US |
dc.subject | Extended belief rule-based system | en_US |
dc.subject | Investment prediction | en_US |
dc.title | Environmental investment prediction using extended belief rule-based system and evidential reasoning rule | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 289 | en_US |
dc.identifier.doi | 10.1016/j.jclepro.2020.125661 | en_US |
dcterms.abstract | A scientific environmental investment prediction plays a crucial role in controlling environmental pollution and avoiding the blind investment of environmental management. However effective environmental investment prediction usually has to fact three challenges about diversiform indicators, insufficient data, and the reliability of prediction models. In the present study, a new prediction model is proposed using the extended belief rule-based system (EBRBS) and evidential reasoning (ER) rule, called ensemble EBRBS model, with the aim to overcome the above challenges for better environmental investment prediction. The proposed ensemble EBRBS model consists of two components: 1) multiple EBRBSs, which are constructed on the basis of not only using various feature selection methods to select representative indicators but also data increment transformation to enrich the training data; 2) an ER rule-based combination method, which utilizes the ER rule to accommodate the weights and reliabilities of different EBRBSs with the predicted outputs of these EBRBSs to have an integrated environmental investment prediction. A detailed case study is then provided for validating the proposed model via extensive experimental and comparison analysis based on the real-world environmental data about 25 environmental indicators for 31 provinces in China ranged from 2005 to 2018. The results demonstrate that the ensemble EBRBS model can be used as an effective model to accurately predict environmental investments. More importantly, the ensemble EBRBS model not only obtains a high accuracy better than some existing prediction models, but also has an excellent robustness compared with others under the situations of excessive indicators and insufficient data. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of cleaner production, 20 Mar. 2021, v. 289, 125661 | en_US |
dcterms.isPartOf | Journal of cleaner production | en_US |
dcterms.issued | 2021-03-20 | - |
dc.identifier.scopus | 2-s2.0-85098844541 | - |
dc.identifier.artn | 125661 | en_US |
dc.description.validate | 202405 bcch | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | EIE-0071 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 55038497 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Hu_Environmental_Investment_Prediction.pdf | Pre-Published version | 1.22 MB | Adobe PDF | View/Open |
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