Backpropagation Vs. Radial Basis Function Neural Model: Rainfall Intensity Classification For Flood Prediction Using Meteorology Data Article Swipe
Soo See Chai
,
Wei Keat Wong
,
Kok Luong Goh
·
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.3844/jcssp.2016.191.200
YOU?
·
· 2016
· Open Access
·
· DOI: https://doi.org/10.3844/jcssp.2016.191.200
Rainfall is one of the important weather variables that vary in space and time. High mean daily rainfall (>30 mm) has a high possibility of resulting in flood. Accurate prediction of this variable would save human lives and properties. Soft computing methods have been widely applied in this field. Among the various soft computing methods, Artificial Neural Network (ANN) is the most commonly used methodology. While numerous ANN algorithms were applied, the most commonly applied are the Backpropagation (BPN) and Radial Basis Function (RFN) models. However, there was no research conducted to verify which model among these two produces a superior result. Therefore, this study will fill this gap. In this study, using the meteorology data, the two ANN models were trained to classify the rainfall intensity based on four different classes: Light (<10 mm), moderate (11-30 mm), heavy (31-50 mm) and very heavy (>51 mm). The architecture of the neural networks models based on the different combination of inputs and number of hidden neurons to obtain the optimum classification were verified in this study. The influence of the number of training data on the classification results was also analyzed. Results obtained showed, in term of classification accuracy, BPN model performed better than the RFN model. However, in term of consistency, the RFN model outperformed BPN model.
Related Topics
Concepts
Backpropagation
Computer science
Artificial neural network
Consistency (knowledge bases)
Artificial intelligence
Field (mathematics)
Flood myth
Radial basis function
Term (time)
Soft computing
Machine learning
Data mining
Pattern recognition (psychology)
Mathematics
Pure mathematics
Theology
Physics
Philosophy
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3844/jcssp.2016.191.200
- http://thescipub.com/pdf/10.3844/jcssp.2016.191.200
- OA Status
- hybrid
- Cited By
- 6
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2342003933
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2342003933Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3844/jcssp.2016.191.200Digital Object Identifier
- Title
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Backpropagation Vs. Radial Basis Function Neural Model: Rainfall Intensity Classification For Flood Prediction Using Meteorology DataWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2016Year of publication
- Publication date
-
2016-04-01Full publication date if available
- Authors
-
Soo See Chai, Wei Keat Wong, Kok Luong GohList of authors in order
- Landing page
-
https://doi.org/10.3844/jcssp.2016.191.200Publisher landing page
- PDF URL
-
https://thescipub.com/pdf/10.3844/jcssp.2016.191.200Direct 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://thescipub.com/pdf/10.3844/jcssp.2016.191.200Direct OA link when available
- Concepts
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Backpropagation, Computer science, Artificial neural network, Consistency (knowledge bases), Artificial intelligence, Field (mathematics), Flood myth, Radial basis function, Term (time), Soft computing, Machine learning, Data mining, Pattern recognition (psychology), Mathematics, Pure mathematics, Theology, Physics, Philosophy, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1, 2021: 1, 2020: 1, 2019: 1Per-year citation counts (last 5 years)
- References (count)
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19Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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