Development and Analysis of Artificial Neural Network Models for Rainfall Prediction by Using Time-Series Data Article Swipe
Neelam Mishra
,
Hemant Kumar Soni
,
Sanjiv Sharma
,
Arvind Kumar Upadhyay
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.5815/ijisa.2018.01.03
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.5815/ijisa.2018.01.03
Time Series data is large in volume, highly dimensional and continuous updating.Time series data analysis for forecasting, is one of the most important aspects of the practical usage.Accurate rainfall forecasting with the help of time series data analysis will help in evaluating drought and flooding situations in advance.In this paper, Artificial Neural Network (ANN) technique has been used to develop one-month and twomonth ahead forecasting models for rainfall prediction using monthly rainfall data of Northern India.In these model, Feed Forward Neural Network (FFNN) using Back Propagation Algorithm and Levenberg-Marquardt training function has been used.The performance of both the models has been assessed based on Regression Analysis, Mean Square Error (MSE) and Magnitude of Relative Error (MRE).Proposed ANN model showed optimistic results for both the models for forecasting and found one month ahead forecasting model perform better than two months ahead forecasting model.This paper also gives some future directions for rainfall prediction and time series data analysis research.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5815/ijisa.2018.01.03
- http://www.mecs-press.org/ijisa/ijisa-v10-n1/IJISA-V10-N1-3.pdf
- OA Status
- diamond
- Cited By
- 125
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2782004273
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- OpenAlex ID
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https://openalex.org/W2782004273Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5815/ijisa.2018.01.03Digital Object Identifier
- Title
-
Development and Analysis of Artificial Neural Network Models for Rainfall Prediction by Using Time-Series DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-01-04Full publication date if available
- Authors
-
Neelam Mishra, Hemant Kumar Soni, Sanjiv Sharma, Arvind Kumar UpadhyayList of authors in order
- Landing page
-
https://doi.org/10.5815/ijisa.2018.01.03Publisher landing page
- PDF URL
-
https://www.mecs-press.org/ijisa/ijisa-v10-n1/IJISA-V10-N1-3.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.mecs-press.org/ijisa/ijisa-v10-n1/IJISA-V10-N1-3.pdfDirect OA link when available
- Concepts
-
Artificial neural network, Computer science, Time series, Mean squared error, Series (stratigraphy), Machine learning, Artificial intelligence, Flooding (psychology), Data mining, Statistics, Mathematics, Paleontology, Biology, Psychology, PsychotherapistTop concepts (fields/topics) attached by OpenAlex
- Cited by
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125Total citation count in OpenAlex
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2025: 5, 2024: 8, 2023: 17, 2022: 27, 2021: 17Per-year citation counts (last 5 years)
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25Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.months | 138 |
| abstract_inverted_index.paper, | 49 |
| abstract_inverted_index.series | 12, 35, 153 |
| abstract_inverted_index.showed | 118 |
| abstract_inverted_index.Forward | 79 |
| abstract_inverted_index.Network | 52, 81 |
| abstract_inverted_index.aspects | 23 |
| abstract_inverted_index.develop | 59 |
| abstract_inverted_index.drought | 42 |
| abstract_inverted_index.monthly | 70 |
| abstract_inverted_index.perform | 134 |
| abstract_inverted_index.results | 120 |
| abstract_inverted_index.volume, | 6 |
| abstract_inverted_index.India.In | 75 |
| abstract_inverted_index.Northern | 74 |
| abstract_inverted_index.Relative | 113 |
| abstract_inverted_index.analysis | 14, 37, 155 |
| abstract_inverted_index.assessed | 101 |
| abstract_inverted_index.flooding | 44 |
| abstract_inverted_index.function | 90 |
| abstract_inverted_index.rainfall | 28, 67, 71, 149 |
| abstract_inverted_index.training | 89 |
| abstract_inverted_index.twomonth | 62 |
| abstract_inverted_index.used.The | 93 |
| abstract_inverted_index.Algorithm | 86 |
| abstract_inverted_index.Analysis, | 105 |
| abstract_inverted_index.Magnitude | 111 |
| abstract_inverted_index.important | 22 |
| abstract_inverted_index.one-month | 60 |
| abstract_inverted_index.practical | 26 |
| abstract_inverted_index.research. | 156 |
| abstract_inverted_index.technique | 54 |
| abstract_inverted_index.Artificial | 50 |
| abstract_inverted_index.Regression | 104 |
| abstract_inverted_index.advance.In | 47 |
| abstract_inverted_index.continuous | 10 |
| abstract_inverted_index.directions | 147 |
| abstract_inverted_index.evaluating | 41 |
| abstract_inverted_index.model.This | 141 |
| abstract_inverted_index.optimistic | 119 |
| abstract_inverted_index.prediction | 68, 150 |
| abstract_inverted_index.situations | 45 |
| abstract_inverted_index.Propagation | 85 |
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| abstract_inverted_index.(MRE).Proposed | 115 |
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| abstract_inverted_index.Levenberg-Marquardt | 88 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5032105005 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.8799999952316284 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.98942877 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |