A Comparative Assessment of Models to Predict Monthly Rainfall in Australia
Accurate rainfall prediction is a challenging task. It is especially challenging in Australia where the climate is highly variable. Australia’s climatic zones range from high rainfall tropical regions in the north to the driest desert region in the interior. The performance of prediction models may...
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Veröffentlicht in: | Water resources management 2018-03, Vol.32 (5), p.1777-1794 |
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description | Accurate rainfall prediction is a challenging task. It is especially challenging in Australia where the climate is highly variable. Australia’s climatic zones range from high rainfall tropical regions in the north to the driest desert region in the interior. The performance of prediction models may vary depending on climatic conditions. It is, therefore, important to assess and compare the performance of these models in different climatic zones. This paper examines the performance of data driven models such as the support vector machines for regression, the multiple linear regression, the
k
-nearest neighbors and the artificial neural networks for monthly rainfall prediction in Australia depending on climatic conditions. Rainfall data with five meteorological variables over the period of 1970–2014 from 24 geographically diverse weather stations are used for this purpose. The prediction performance of each model was evaluated by comparing observed and predicted rainfall using various measures for prediction accuracy. |
doi_str_mv | 10.1007/s11269-018-1903-y |
format | Article |
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k
-nearest neighbors and the artificial neural networks for monthly rainfall prediction in Australia depending on climatic conditions. Rainfall data with five meteorological variables over the period of 1970–2014 from 24 geographically diverse weather stations are used for this purpose. The prediction performance of each model was evaluated by comparing observed and predicted rainfall using various measures for prediction accuracy.</description><identifier>ISSN: 0920-4741</identifier><identifier>EISSN: 1573-1650</identifier><identifier>DOI: 10.1007/s11269-018-1903-y</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Artificial neural networks ; Atmospheric Sciences ; Civil Engineering ; Climatic conditions ; Climatic zones ; Earth and Environmental Science ; Earth Sciences ; Environment ; Geotechnical Engineering & Applied Earth Sciences ; Hydrogeology ; Hydrologic data ; Hydrology/Water Resources ; Neural networks ; Performance prediction ; Prediction models ; Rain ; Rainfall ; Support vector machines ; Tropical climate ; Tropical environment ; Tropical environments ; Weather stations</subject><ispartof>Water resources management, 2018-03, Vol.32 (5), p.1777-1794</ispartof><rights>Springer Science+Business Media B.V., part of Springer Nature 2018</rights><rights>Water Resources Management is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c316t-8d7bac292ee2ed79dfefda6d62483cae03924d4d0e07dc03461a3e850e9d6b1e3</citedby><cites>FETCH-LOGICAL-c316t-8d7bac292ee2ed79dfefda6d62483cae03924d4d0e07dc03461a3e850e9d6b1e3</cites><orcidid>0000-0003-2075-1699</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11269-018-1903-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11269-018-1903-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Bagirov, Adil M.</creatorcontrib><creatorcontrib>Mahmood, Arshad</creatorcontrib><title>A Comparative Assessment of Models to Predict Monthly Rainfall in Australia</title><title>Water resources management</title><addtitle>Water Resour Manage</addtitle><description>Accurate rainfall prediction is a challenging task. It is especially challenging in Australia where the climate is highly variable. Australia’s climatic zones range from high rainfall tropical regions in the north to the driest desert region in the interior. The performance of prediction models may vary depending on climatic conditions. It is, therefore, important to assess and compare the performance of these models in different climatic zones. This paper examines the performance of data driven models such as the support vector machines for regression, the multiple linear regression, the
k
-nearest neighbors and the artificial neural networks for monthly rainfall prediction in Australia depending on climatic conditions. Rainfall data with five meteorological variables over the period of 1970–2014 from 24 geographically diverse weather stations are used for this purpose. 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k
-nearest neighbors and the artificial neural networks for monthly rainfall prediction in Australia depending on climatic conditions. Rainfall data with five meteorological variables over the period of 1970–2014 from 24 geographically diverse weather stations are used for this purpose. The prediction performance of each model was evaluated by comparing observed and predicted rainfall using various measures for prediction accuracy.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11269-018-1903-y</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0003-2075-1699</orcidid></addata></record> |
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subjects | Artificial neural networks Atmospheric Sciences Civil Engineering Climatic conditions Climatic zones Earth and Environmental Science Earth Sciences Environment Geotechnical Engineering & Applied Earth Sciences Hydrogeology Hydrologic data Hydrology/Water Resources Neural networks Performance prediction Prediction models Rain Rainfall Support vector machines Tropical climate Tropical environment Tropical environments Weather stations |
title | A Comparative Assessment of Models to Predict Monthly Rainfall in Australia |
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