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首页> 外文期刊>International Journal of Water >Application of global precipitation dataset for drought monitoring and forecasting over the Lake Urmia basin with the GA-SVR model
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Application of global precipitation dataset for drought monitoring and forecasting over the Lake Urmia basin with the GA-SVR model

机译：GA-SVR模型在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

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摘要

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.
机译：在本研究中，评估气候研究单位（CRU）降水数据的准确性作为替代来源，而不是用于监测1984年至2013年这段时期乌尔米亚湖盆地地区干旱的原位数据。使用算法支持向量回归（GA-SVR）模型来预测长达四个月的干旱状况。结果表明，CRU数据在干旱监测中具有可接受的准确性，因此在至少75％的情况下，通过观测数据和CRU数据分类的监测干旱之间没有差异。在预测部分，结果显示了两种一般模式。第一种模式表示预测准确性的下降趋势，即提前期的交货时间增加。第二种模式揭示了预测准确性的上升趋势，即SPI规模的增加。

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