A Recursive Approach to Long-Term Prediction of Monthly Precipitation Using Genetic Programming Article Swipe
Suning Liu
,
Haiyun Shi
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1007/s11269-018-2169-0
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1007/s11269-018-2169-0
Precipitation is regarded as the basic component of the global hydrological cycle. This study develops a recursive approach to long-term prediction of monthly precipitation using genetic programming (GP), taking the Three-River Headwaters Region (TRHR) in China as the study area. The daily precipitation data recorded at 29 meteorological stations during 1961–2014 are collected, among which the data during 1961–2000 are for calibration and the remaining data are for validation. To develop this approach, first, the preliminary estimations of annual precipitation are computed based on a statistical method. Second, the percentage of the monthly precipitation for each month of a year is calculated as the mean monthly precipitation divided by the mean annual precipitation during the study period, and then the preliminary estimation of monthly precipitation for each month of a year is obtained. Third, since GP can be used to improve the prediction results through establishing the relationship of the observations with the preliminary estimations at the past and current times, it is adopted to improve the preliminary estimations. The calibration and validation results reveal that the recursive approach involving GP can provide the more accurate predictions of monthly precipitation. Finally, this approach is used to predict the monthly precipitation over the TRHR till 2050. Overall, the proposed method and the obtained results will enhance our understanding and facilitate future studies regarding the long-term prediction of precipitation in such regions.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s11269-018-2169-0
- https://link.springer.com/content/pdf/10.1007/s11269-018-2169-0.pdf
- OA Status
- hybrid
- Cited By
- 19
- References
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2904628560
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2904628560Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s11269-018-2169-0Digital Object Identifier
- Title
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A Recursive Approach to Long-Term Prediction of Monthly Precipitation Using Genetic ProgrammingWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2018Year of publication
- Publication date
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2018-12-15Full publication date if available
- Authors
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Suning Liu, Haiyun ShiList of authors in order
- Landing page
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https://doi.org/10.1007/s11269-018-2169-0Publisher landing page
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https://link.springer.com/content/pdf/10.1007/s11269-018-2169-0.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s11269-018-2169-0.pdfDirect OA link when available
- Concepts
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Precipitation, Environmental science, Term (time), Genetic programming, Climatology, Calibration, Quantitative precipitation forecast, Quantitative precipitation estimation, Meteorology, Statistics, Computer science, Mathematics, Geography, Geology, Machine learning, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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19Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2, 2022: 3, 2021: 8, 2020: 5, 2019: 1Per-year citation counts (last 5 years)
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57Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W1529146523, https://openalex.org/W2077167237, https://openalex.org/W2036083340, https://openalex.org/W1544580316, https://openalex.org/W2019079424, https://openalex.org/W2000896988, https://openalex.org/W2732218308, https://openalex.org/W2587802610, https://openalex.org/W2181641430, https://openalex.org/W1884051499, https://openalex.org/W1983810184, https://openalex.org/W2128736991, https://openalex.org/W2056002333, https://openalex.org/W2112819718, https://openalex.org/W2078280512, https://openalex.org/W2085858708, https://openalex.org/W2102588958, https://openalex.org/W2085585287, https://openalex.org/W2068631161, https://openalex.org/W2580056466, https://openalex.org/W2092617489, https://openalex.org/W2160203977, https://openalex.org/W2145908487, https://openalex.org/W2081557600, https://openalex.org/W2799389225, https://openalex.org/W2125953913, https://openalex.org/W2116671676, https://openalex.org/W2797715825, https://openalex.org/W2137582175, https://openalex.org/W2080152519, https://openalex.org/W2052981083, https://openalex.org/W2033904036, https://openalex.org/W2053035314, https://openalex.org/W2011231125, https://openalex.org/W2070532408, https://openalex.org/W2322782311, https://openalex.org/W2592238886, https://openalex.org/W2176929435, https://openalex.org/W4249206705, https://openalex.org/W2079029817, https://openalex.org/W1973249946, https://openalex.org/W1994180832, https://openalex.org/W2139108779, https://openalex.org/W2148195638, https://openalex.org/W2803892425, https://openalex.org/W2305038149, https://openalex.org/W2594282743, https://openalex.org/W1976657711, https://openalex.org/W617039848, https://openalex.org/W2043685855, https://openalex.org/W2091361900, https://openalex.org/W1984823613, https://openalex.org/W1517016597, https://openalex.org/W1548396839, https://openalex.org/W1576818901, https://openalex.org/W4245407374, https://openalex.org/W1482827835 |
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