Advance drought prediction through rainfall forecasting with hybrid deep learning model Article Swipe
Brij B. Gupta
,
Akshat Gaurav
,
Razaz Waheeb Attar
,
Varsha Arya
,
Shavi Bansal
,
Ahmed Alhomoud
,
Kwok Tai Chui
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-80099-6
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-024-80099-6
Drought is a natural disaster that can affect a larger area over time. Damage caused by the drought can only be reduced through its accurate prediction. In this context, we proposed a hybrid stacked model for rainfall prediction, which is crucial for effective drought forecasting and management. In the first layer of stacked models, Bi-directional LSTM is used to extract the features, and then in the second layer, the LSTM model will make the predictions. The model captures complex temporal dependencies by processing multivariate time series data in both forward and backward directions using bi-directional LSTM layers. Trained with the Mean Squared Error loss and Adam optimizer, the model demonstrates improved forecasting accuracy, offering significant potential for proactive drought management.
Related Topics
Concepts
Computer science
Context (archaeology)
Multivariate statistics
Layer (electronics)
Artificial intelligence
Mean squared error
Time series
Deep learning
Series (stratigraphy)
Machine learning
Mean squared prediction error
Data mining
Statistics
Mathematics
Geology
Organic chemistry
Chemistry
Paleontology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-024-80099-6
- https://www.nature.com/articles/s41598-024-80099-6.pdf
- OA Status
- gold
- Cited By
- 8
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405371058
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405371058Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-024-80099-6Digital Object Identifier
- Title
-
Advance drought prediction through rainfall forecasting with hybrid deep learning modelWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-13Full publication date if available
- Authors
-
Brij B. Gupta, Akshat Gaurav, Razaz Waheeb Attar, Varsha Arya, Shavi Bansal, Ahmed Alhomoud, Kwok Tai ChuiList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-024-80099-6Publisher landing page
- PDF URL
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https://www.nature.com/articles/s41598-024-80099-6.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.nature.com/articles/s41598-024-80099-6.pdfDirect OA link when available
- Concepts
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Computer science, Context (archaeology), Multivariate statistics, Layer (electronics), Artificial intelligence, Mean squared error, Time series, Deep learning, Series (stratigraphy), Machine learning, Mean squared prediction error, Data mining, Statistics, Mathematics, Geology, Organic chemistry, Chemistry, PaleontologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8Per-year citation counts (last 5 years)
- References (count)
-
24Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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