Monthly Rainfall Forecasting Using One-Dimensional Deep Convolutional Neural Network Article Swipe
Ali Haidar
,
Brijesh Verma
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1109/access.2018.2880044
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1109/access.2018.2880044
Rainfall prediction targets the determination of rainfall conditions over a specific location. It is considered vital for the agricultural industry and other industries. In this paper, we propose a new forecasting method that uses a deep convolutional neural network (CNN) to predict monthly rainfall for a selected location in eastern Australia. To our knowledge, this is the first time applying a deep CNN in predicting monthly rainfall. The proposed approach was compared against the Australian Community Climate and Earth-System Simulator-Seasonal Prediction System (ACCESS), which is a forecasting model released by the Bureau of Meteorology. In addition, the CNN was compared against a conventional multi-layered perceptron (MLP). The better mean absolute error, root mean square error (RMSE), Pearson correlation (r), and Nash Suttcliff coefficient of efficiency values were obtained with the proposed CNN. A difference of 37.006 mm was obtained in terms of RMSE compared with ACCESS and 15.941 compared with conventional MLP. Further investigation revealed that the CNN was generally performing better in months with higher annual averages, while ACCESS was performing better in months with low annual averages. The generated output is promising and can be widely extended in this type of applications.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2018.2880044
- OA Status
- gold
- Cited By
- 145
- References
- 71
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2900293687
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2900293687Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2018.2880044Digital Object Identifier
- Title
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Monthly Rainfall Forecasting Using One-Dimensional Deep Convolutional Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-01-01Full publication date if available
- Authors
-
Ali Haidar, Brijesh VermaList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2018.2880044Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/access.2018.2880044Direct OA link when available
- Concepts
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Mean squared error, Convolutional neural network, Artificial neural network, Computer science, Multilayer perceptron, Perceptron, Correlation coefficient, Deep learning, Mean absolute error, Weather forecasting, Meteorology, Artificial intelligence, Statistics, Machine learning, Mathematics, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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145Total citation count in OpenAlex
- Citations by year (recent)
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2025: 17, 2024: 37, 2023: 32, 2022: 29, 2021: 13Per-year citation counts (last 5 years)
- References (count)
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71Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2020098782, https://openalex.org/W2006976149, https://openalex.org/W2092556113, https://openalex.org/W2154127369, https://openalex.org/W2158638608, https://openalex.org/W2040916393, https://openalex.org/W2014202046, https://openalex.org/W2166358344, https://openalex.org/W2109796642, https://openalex.org/W2597172628, https://openalex.org/W2023430894, https://openalex.org/W2058998445, https://openalex.org/W43382659, https://openalex.org/W6713134421, https://openalex.org/W2015686972, https://openalex.org/W6674330103, https://openalex.org/W2113958167, https://openalex.org/W2038782284, https://openalex.org/W2531416169, https://openalex.org/W6631158986, https://openalex.org/W1124582155, https://openalex.org/W2569140349, https://openalex.org/W1999461467, https://openalex.org/W2287295854, https://openalex.org/W4231333952, https://openalex.org/W2062087947, https://openalex.org/W6684191040, https://openalex.org/W2727743810, https://openalex.org/W2809137653, https://openalex.org/W2801370692, https://openalex.org/W2611049230, https://openalex.org/W2476370993, https://openalex.org/W6696429117, https://openalex.org/W2587894229, https://openalex.org/W2750021257, https://openalex.org/W2799610518, https://openalex.org/W2343898460, https://openalex.org/W6746150385, https://openalex.org/W2094152352, https://openalex.org/W1519807182, https://openalex.org/W6696926919, https://openalex.org/W2062304619, https://openalex.org/W2171402736, https://openalex.org/W1989977194, https://openalex.org/W1984308338, https://openalex.org/W4251494627, https://openalex.org/W2066456070, https://openalex.org/W2177959459, https://openalex.org/W2963334963, https://openalex.org/W2294556236, https://openalex.org/W1970360626, https://openalex.org/W800658455, https://openalex.org/W2572609942, https://openalex.org/W2754332904, https://openalex.org/W2331446345, https://openalex.org/W2518675717, https://openalex.org/W2164502060, https://openalex.org/W1013913889, https://openalex.org/W2783188026, https://openalex.org/W2039049978, https://openalex.org/W2953384591, https://openalex.org/W1519843225, https://openalex.org/W2970564449, https://openalex.org/W2402144811, https://openalex.org/W2297999069, https://openalex.org/W2901378022, https://openalex.org/W2766787110, https://openalex.org/W2288074780, https://openalex.org/W2095705004, https://openalex.org/W2163605009, https://openalex.org/W2165220437 |
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| abstract_inverted_index.in | 48, 63, 139, 162, 173, 189 |
| abstract_inverted_index.is | 13, 55, 84, 182 |
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| abstract_inverted_index.CNN | 62, 97, 157 |
| abstract_inverted_index.The | 67, 106, 179 |
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| abstract_inverted_index.was | 70, 98, 137, 158, 170 |
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| abstract_inverted_index.37.006 | 135 |
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