Poisson probability distribution analysis of Makurdi and Abeokuta rainfalls

Asani M. Afolabi, Lukman Salihu, Sani Salaudreen, O. Stephen, O. Adesola
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Abstract

Early information for sustainable utilization of water resources through poisson probability distribution of rainfall is an important regulatory measure for flood control and water security management. As a follow-up to our previous studies on distributions, this paper reports statistical goodness-of-fit evaluations of selected rainfall data. It is the utilization of the maximum likelihood method (MLM) for the poisson probability distribution (PPD)of selected rainfall data. The numerically estimated constant of the density of PPD was estimated by the MLM, and Microsoft Excel Solver (MES). These estimated constants were used to compute probabilities of poisson distributions. The computed probabilities using the constants obtained were evaluated statistically (analysis of variance, (ANOVA), relative error, model of' selection criterion (MSC), Coefficient of Determination (CD) and Correlation coefficient (R). The study established that the poisson probability distribution’s parameter (p) was the average of the logarithm to base 10 of rainfall using the MLM and MES estimators. The constants were found to be 0.665 and 0.535 for Makurdi, 0.695 and 0.478 for Abeokuta using MLM and MES, respectively. The relative errors were found to be 0.479 and 0.743, and 1.141 and 1.509 for Makurdi and Abeokuta using MLM and MES, respectively. The correlation coefficient for Makurdi and Abeokuta using MLM and MES were found to be 0.876 and 0.800, and 0.269 and 0.341, respectively. It was concluded that the MLM constant was better than MES based on the values of MSC, CD, relative error and R. MLM predicted Weibull probability of rainfall intensity better than MES. Utilization of PPD in the estimation of rainfall intensity will help in the prediction of rainfall for agriculture in attaining sustainable goal 2 (zero hunger), regulatory measures for flood control and water security management. There is a need to evaluate MLM and other probability distributions to further assist in attaining sustainable development goals.
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Makurdi和Abeokuta降雨的泊松概率分布分析
通过降雨泊松概率分布来获取水资源可持续利用的早期信息是防洪和水安全管理的重要调控措施。作为我们之前关于分布的研究的后续,本文报告了所选降雨数据的统计拟合优度评估。它是对所选降水资料泊松概率分布(PPD)的最大似然方法的应用。利用MLM软件和Microsoft Excel Solver (MES)软件对PPD的密度进行了数值估计。这些估计的常数被用来计算泊松分布的概率。利用得到的常数计算概率进行统计评估(方差分析(ANOVA)、相对误差、模型选择标准(MSC)、决定系数(CD)和相关系数(R))。研究确定泊松概率分布的参数(p)是使用MLM和MES估计器对降雨量的对数到基底10的平均值。利用MLM和MES对Makurdi和Abeokuta的分析结果分别为0.665和0.535,0.695和0.478。MLM和MES对Makurdi和Abeokuta的相对误差分别为0.479和0.743,1.141和1.509。MLM和MES对Makurdi和Abeokuta的相关系数分别为0.876和0.800,0.269和0.341。基于MSC、CD、相对误差和r值,MLM对降雨强度威布尔概率的预测优于MES。利用PPD估算降雨强度将有助于预测农业降雨,以实现可持续目标2(零饥饿)、洪水控制和水安全管理的监管措施。有必要评价传销和其他概率分布,以进一步协助实现可持续发展目标。
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发文量
15
审稿时长
12 weeks
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