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The Modified DISPRIN Model for Transforming Daily Rainfall-Runoff Data Series on a Small Watershed in Archipelagic Region

机译:用于在群体区域的小流域中转换日常降雨径流数据系列的改进的解剖模型

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The existence of the translation effect component on the application of the original Dee Investigation Simulation Program for Regulating Network (DISPRIN) model would be counter-productive when applied to rainfall-runoff analysis on small watersheds that have the level of sharp fluctuations that commonly occur in tropical islands. Modifying the original DISPRIN model by ignoring the components proved to mask existing weaknesses. This article tries to compare the performance of the original DISPRIN model and the modified DISPRIN model in the case of the transformation of rainfall data series into discharge data series on a daily period. The calibration process of the parameters of both models uses the evolution differential algorithm (DE). The case study is Lesti watershed at the control point of AWLR Tawangrejeni station (319.14 km2) located in East Java, Indonesia. The test model uses 10-year daily data sets, from January 1, 2007, to December 31, 2016. Data series from 2007 to 2013 as a training data set used for the process of model calibration and model validation, data series from 2014 to 2016 as a test data set for model verification. The results show that the modified DISPRIN model is more effective than the original DISPRIN model in terms of accuracy and iteration time in achieving convergent conditions. The original DISPRIN model was able to respond to fluctuations in a seasonal flow, but was unable to respond to the sharp fluctuations in daily flows. The modified DISPRIN model can fix that vulnerability and can generate an NSE > 0.8 value in the validation and verification phase.
机译:在适用于调节网络(DISPRIN)模型的原始DEE调查模型应用的翻译效果组件将是对小流域的降雨径流分析时的反效率,这些分析器具有普遍发生的剧烈波动水平的水平热带群岛。通过忽略证明的组件来修改原始的DICLIN模型,以掩盖现有的弱点。本文试图将原始DISPRIN模型和修改的DIARPRIN模型的性能进行比较日常时期放电数据系列的降雨数据系列的情况。两种模型参数的校准过程使用进化差分算法(DE)。案例研究是位于印度尼西亚东爪哇省东爪哇省的AWLR Tawangrejeni Station(319.14 Km2)的控制点的案例研究。测试模型使用10年期间数据集,2007年1月1日至2016年12月31日。数据系列从2007年到2013年作为用于模型校准和模型验证,从2014年的数据系列的培训数据集。 2016作为用于模型验证的测试数据。结果表明,在实现收敛条件方面,修改的DISPRIN模型比原始DICRIN模型更有效。原始的DISPRIN模型能够响应季节性流动的波动,但无法响应日常流动的剧烈波动。修改的DISPRIN模型可以修复该漏洞,可以在验证和验证阶段生成NSE> 0.8值。

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