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Modelling Rainfall Patterns in Meru and Embu Regions Using ARIMA Models

机译:利用Arima模型在Meru和Embu区建模降雨模式

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摘要

Rainfall is the natural source of water, it has greater impact on agricultural activities and domestic consumptions. Since Meru and Embu regions are agricultural zones relying heavily on rainfed agriculture, it is important for farmers to know rainfall patterns prevailing in their regions.In this study we model rainfall patterns in Meru and Embu regions of Kenya using Monthly and yearly rainfall data. ARIMA model was developed using Box-Jenkins (BJ) Methodology and fit to monthly and yearly average amount of rainfall. The data were examined to check for the most appropriate class of ARIMA processes. This was done by selecting the order of the consecutive and seasonal differencing. The auto-correlation function (ACF) and the partial autocorrelation function (PACF) are the most important elements of time series analysis. Using the AIC criterion ARIMA (1,1,1)(0,1,1) 12 was identified as the best model . This model was used to forecast monthly rainfall patterns for five years and found that future rainfall patterns will change with time as contributed by many factors. It was recommended that, future researchers should consider zoning regions, identify other factors contributing to change in rainfall patterns and apply developed Arima model.
机译:降雨是水的自然来源,它对农业活动和国内消费产生了更大的影响。由于Meru和Embu地区是农业区依赖于雨量农业的农业区,因此对于农民来说,了解他们的地区的降雨模式。在这项研究中,我们使用每月和每年降雨量的肯尼亚的Meru和Embu地区的降雨模式。 Arima模型是使用Box-Jenkins(BJ)方法开发的,并适合每月和每年平均降雨量。检查数据以检查最合适的Arima流程。这是通过选择连续和季节性差异的顺序来完成的。自相关函数(ACF)和部分自相关函数(PACF)是时间序列分析最重要的元素。使用AIC标准ARIMA(1,1,1)(0.1,1)12被识别为最佳模型。该模型用于预测每月降雨模式五年,发现未来的降雨模式将随着许多因素的贡献而变化。建议,未来的研究人员应考虑分区区域,确定其他因素有助于降雨模式的变化,并应用开发的ARIMA模型。

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