基于GA-SVR模型的全球降水数据集在乌尔米亚湖流域干旱监测预报中的应用

Q2 Social Sciences International Journal of Water Pub Date : 2018-08-01 DOI:10.1504/IJW.2018.10014781
Edris Ahmadebrahimpour, B. Aminnejad, K. Khalili
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引用次数: 2

摘要

以1984 - 2013年乌尔米亚湖流域地区为研究对象,对气候研究单元(CRU)降水资料作为替代现场降水资料监测干旱的准确性进行了评价。随后,利用遗传算法-支持向量回归(GA-SVR)模型预测未来4个月的干旱状况。结果表明,CRU数据在干旱监测中具有可接受的准确性,在至少75%的情况下,通过观测数据分类的干旱监测与CRU数据之间没有差异。在预测部分,结果显示出两种一般模式。第一种模式表明,随着预测前交货期的增加,预测准确性呈下降趋势;第二种模式随着SPI尺度的增加,预测精度呈上升趋势。
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Application of global precipitation dataset for drought monitoring and forecasting over the Lake Urmia basin with the GA-SVR model
In the present study, the accuracy of the climate research unit (CRU) precipitation data was assessed as an alternative source instead of in situ data for monitoring the drought in the Lake Urmia Basin area during the period from 1984 to 2013. Later, a genetic algorithm-support vector regression (GA-SVR) model was utilised in order to forecast drought conditions up to four months ahead. The results demonstrated that the CRU data had acceptable accuracy in drought monitoring so that in at least 75% of the cases, there was no difference between the monitored drought classed through observed data and CRU data. In the forecasting section, the results showed two general patterns. The first pattern indicated a descending trend of forecast accuracy with an increase in the lead-times ahead of forecasts; the second pattern revealed the ascending trend of forecast accuracy, with an increase in the SPI scale.
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来源期刊
International Journal of Water
International Journal of Water Social Sciences-Geography, Planning and Development
CiteScore
0.40
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期刊介绍: The IJW is a fully refereed journal, providing a high profile international outlet for analyses and discussions of all aspects of water, environment and society.
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