首页> 外文期刊>American Journal of Engineering Research >Frequency Analysis of the Monthly Rainfall Data at Sulaimania Region, Iraq
【24h】

Frequency Analysis of the Monthly Rainfall Data at Sulaimania Region, Iraq

机译:伊拉克苏拉马尼亚地区月降雨量数据的频率分析

获取原文
       

摘要

Different frequency distributions models were fitted to the monthly rainfall data in Sulaimania region, north Iraq. Three rainfall gauging stations data were used, Sulaimania city, Dokan Dam, and Derbendikhan Dam metrological stations, for the period (1984-2010). The distributions models fitted are of Normal, Log-normal, Wiebull, Exponential and Two parameters Gamma type. The Kolmogorov-Smirnov test was used to evaluate the goodness of fit. The fittings were done for the overall data and for each month separately. The Gamma, Exponential and Weibull distributions were found as the best fits for the three stations respectively for the overall models, while for the monthly models different distribution type was found as the best fit for each month and each station, however the Gamma distributions was found to have the highest percent of best fit. The best fitted distributions were used to forecast three sets of monthly rainfall data for each station and compared to the observed ones for the last 7- years of data. The t-test,F-test and Kolmogorov- Smirnov test indicate the capability of these models to produce data that has the same frequency distribution of the observed one. Comparison between the performances of the overall and periodic models reveals that there no distinguishable improvement of the monthly model over the overall one
机译:伊拉克北部苏莱马尼亚地区的月降雨量数据拟合了不同的频率分布模型。在此期间(1984-2010年),使用了三个降雨测量站数据:苏莱曼尼亚市,多坎大坝和德本迪汗大坝计量站。拟合的分布模型为正态,对数正态,Wiebull,指数和两个参数Gamma类型。 Kolmogorov-Smirnov检验用于评估拟合优度。分别对整体数据和每个月进行拟合。 Gamma,指数和Weibull分布被认为是整体模型分别适合三个站点的最佳拟合,而月度模型则发现不同的分布类型适合每个月和每个站点的最佳拟合,但是找到了Gamma分布拥有最合适的最高百分比。最佳拟合的分布用于预测每个站点的三组月降雨量数据,并与最近7年的观测数据进行比较。 t检验,F检验和Kolmogorov-Smirnov检验表明这些模型具有产生与观察到的频率分布相同频率分布的数据的能力。总体模型和定期模型的性能比较表明,与总体模型相比,每月模型没有明显的改进

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号