Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1016/j.jhydrol.2018.05.030 |
Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions | |
Aksoy, Hafzullah1; Dahamsheh, Ahmad2 | |
通讯作者 | Aksoy, Hafzullah |
来源期刊 | JOURNAL OF HYDROLOGY
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ISSN | 0022-1694 |
EISSN | 1879-2707 |
出版年 | 2018 |
卷号 | 562页码:758-779 |
英文摘要 | For forecasting monthly precipitation in an arid region, the feed forward back-propagation, radial basis function and generalized regression artificial neural networks (ANNs) are used in this study. The ANN models are improved after incorporation of a Markov chain-based algorithm (MC-ANNs) with which the percentage of dry months is forecasted perfectly, thus generation of any non-physical negative precipitation is eliminated. Due to the fact that recorded precipitation time series are usually shorter than the length needed for a proper calibration of ANN models, synthetic monthly precipitation data are generated by Thomas-Fiering model to further improve the performance of forecasting. For case studies from Jordan, it is seen that only a slightly better performance is achieved with the use of MC and synthetic data. A conditional statement is, therefore, established and imbedded into the ANN models after the incorporation of MC and support of synthetic data, to substantially improve the ability of the models for forecasting monthly precipitation in arid regions. |
英文关键词 | Arid region Artificial neural networks Intermittent precipitation Markov chain Synthetic data Thomas-Fiering model |
类型 | Article |
语种 | 英语 |
国家 | Turkey ; Jordan |
收录类别 | SCI-E |
WOS记录号 | WOS:000438003000059 |
WOS关键词 | MONTHLY RAINFALL ; TIME-SERIES ; EVAPOTRANSPIRATION ; PREDICTION ; FLOW ; JORDAN |
WOS类目 | Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources |
WOS研究方向 | Engineering ; Geology ; Water Resources |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/211066 |
作者单位 | 1.Istanbul Tech Univ, Dept Civil Engn, TR-34469 Istanbul, Turkey; 2.Al Hussein Bin Talal Univ, Dept Civil Engn, Maan, Jordan |
推荐引用方式 GB/T 7714 | Aksoy, Hafzullah,Dahamsheh, Ahmad. Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions[J],2018,562:758-779. |
APA | Aksoy, Hafzullah,&Dahamsheh, Ahmad.(2018).Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions.JOURNAL OF HYDROLOGY,562,758-779. |
MLA | Aksoy, Hafzullah,et al."Markov chain-incorporated and synthetic data-supported conditional artificial neural network models for forecasting monthly precipitation in arid regions".JOURNAL OF HYDROLOGY 562(2018):758-779. |
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