Climate Change and Urbanization Impact on Hydropower Plant by Neural Network-Based Decision-Making Methods: Identification of the Most Significant Parameter Article Swipe
Priyanka Majumder
,
Mrinmoy Majumder
,
Apu Kumar Saha
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1007/s41101-018-0048-4
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1007/s41101-018-0048-4
The change in climate and rate of urbanization affects and unbalances the equilibrium between the demand and supply of energy resources. Hydropower plants are one of the most affected systems. But not all the operational parameters get influenced by this change. The significance varies between the parameters and indicators. Until now, there are no studies which try to identify the most significant parameters which get mostly affected by the climatic as well as urbanization uncertainties. As a result, the present investigation aims to detect the main indicator which can represent the operational status of hydropower plants under climatic and urbanization impacts. The objective multi criteria decision-making methods followed the implementation of polynomial neural networks to estimate the significance of the indicators. Although the list of indicator is not exhaustive, the said features are mostly consulted before concluding on the operational status of the power plant. According to the results, the most significant parameter was efficiency of generator. In this aspect, it is to be noted that harmonic mean hierarchy process (HMHP) and measuring attractiveness by a categorical-based evaluation technique (MACBETH) methods were used as multi criteria decision-making methods and polynomial neural networks were used to predict the function which will represent the present status of a power plant. In this study, we also used sensitivity analysis.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s41101-018-0048-4
- https://link.springer.com/content/pdf/10.1007/s41101-018-0048-4.pdf
- OA Status
- hybrid
- Cited By
- 10
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2801468387
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- OpenAlex ID
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https://openalex.org/W2801468387Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s41101-018-0048-4Digital Object Identifier
- Title
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Climate Change and Urbanization Impact on Hydropower Plant by Neural Network-Based Decision-Making Methods: Identification of the Most Significant ParameterWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-05-10Full publication date if available
- Authors
-
Priyanka Majumder, Mrinmoy Majumder, Apu Kumar SahaList of authors in order
- Landing page
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https://doi.org/10.1007/s41101-018-0048-4Publisher landing page
- PDF URL
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https://link.springer.com/content/pdf/10.1007/s41101-018-0048-4.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://link.springer.com/content/pdf/10.1007/s41101-018-0048-4.pdfDirect OA link when available
- Concepts
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Hydropower, Urbanization, Computer science, Artificial neural network, Climate change, Identification (biology), Categorical variable, Environmental science, Artificial intelligence, Machine learning, Engineering, Economics, Ecology, Electrical engineering, Biology, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
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2024: 2, 2022: 2, 2021: 1, 2020: 2, 2019: 3Per-year citation counts (last 5 years)
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30Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.results, | 148 |
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| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5008319500 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I196486160 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.6399999856948853 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.73909387 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |