The main objective of this study is to evaluate the ability to forecast seasonal and non-seasonal monthly reference plant water consumption (ET₀) with the Box–Jenkins approach (autoregressive integrated moving average), the abbreviation of which is ARIMA (indicated as SARIMA for seasonal series). This target was achieved by using the necessary climate parameters obtained from the Ankara meteorology station to calculate the monthly ET₀ data sequences by using the Penman–Monteith relationship. Of the 12 months’ non-seasonal ET₀ data, only 4 months achieved all the conditions expected in simulation after satisfying the stationary condition. The ARIMA (SARIMA) (1,0,1) (3,1,0) model produced the best results among all models providing the necessary conditions for the seasonal series. When comparing the results of ET₀ estimation from this model to that of the non-seasonal series, it was determined that using the seasonal series rather than making an estimate based on the non-seasonal series provided more successful results, and the model giving the most successful forecast for the study is the SARIMA model above.

中文翻译：

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随机模型预测作物参考蒸散量的能力*

本研究的主要目的是评估使用 Box-Jenkins 方法（自回归综合移动平均）预测季节性和非季节性每月参考植物用水量 (ET ₀ ) 的能力，其缩写为 ARIMA（表示为 SARIMA对于季节性系列）。这一目标是通过使用从安卡拉气象站获得的必要气候参数来实现的，通过使用 Penman-Monteith 关系计算每月 ET ₀数据序列。12 个月的非季节性 ET ₀数据显示，在满足平稳条件后，仅用了 4 个月就达到了模拟中预期的所有条件。ARIMA (SARIMA) (1,0,1) (3,1,0) 模型在所有模型中产生了最好的结果，为季节性序列提供了必要的条件。当将该模型的 ET ₀估计结果与非季节性序列的结果进行比较时，确定使用季节性序列而不是基于非季节性序列进行估计提供了更成功的结果，并且给出的模型该研究最成功的预测是上面的 SARIMA 模型。