The forecasting of tide or ebb elevation is a conventional issue. However, prediction of different components of tidal cycle in tidal rivers is a new aspect in geology and river engineering. For this purpose, this study utilizes the Gene Expression Programming (GEP) method in the Khosro-Abad, Khorramshahr, and Arvand Rood tidal stations (from 2001 to 2008). For short-term forecasts, normality and stationary of data time series are necessary. Existence of trends and skewness reduces accuracy of short-term predictions. Therefore, the modified Mann-Kendall trend test (MK3) method was applied and this test did not show any significant trend in daily tidal data time series. Due to the large skewness in a number of data time series, the Box-Cox transformation function was applied for minimization of skewness coefficient. Then, Augmented Dickey-Fuller (ADF) and Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) tests showed that different normalized tidal data time series are stationary. The autocorrelation function (ACF) and partial autocorrelation function (PACF) diagrams illustrate that maximum number of effective lags is 3 days and the GEP must use data of 1 to 3 days ago. The GEP method stated different equations for forecasting of different components of tidal cycle in three tidal stations. By comparison between observed data and predicted values by these derived equations, it is observed that the range of R and root mean square error (RMSE) are from 0.867 to 0.944 and from 0.058 to 0.149 m.

潮汐或退潮高度的预报是一个常规问题。然而，对潮汐河中潮汐周期的不同组成部分的预测是地质学和河流工程学的一个新方面。为此，本研究利用了Khosro-Abad，Khorramshahr和Arvand Rood潮汐站（2001年至2008年）的基因表达编程（GEP）方法。对于短期预测，数据时间序列的正态性和平稳性是必需的。趋势和偏度的存在降低了短期预测的准确性。因此，应用了改进的Mann-Kendall趋势检验（MK3）方法，该检验在每日潮汐数据时间序列中未显示任何显着趋势。由于在多个数据时间序列中存在较大的偏度，因此将Box-Cox变换函数应用于最小化偏度系数。然后，增强的Dickey-Fuller（ADF）和Kwiatkowski，Phillips，Schmidt和Shin（KPSS）测试表明，不同的标准化潮汐数据时间序列是固定的。自相关函数（ACF）和部分自相关函数（PACF）图表说明，有效滞后的最大数量为3天，并且GEP必须使用1至3天之前的数据。GEP方法提出了用于预测三个潮汐站不同潮汐周期分量的不同方程。通过这些推导方程比较观察到的数据和预测值，可以发现 自相关函数（ACF）和部分自相关函数（PACF）图表说明，有效滞后的最大数量为3天，并且GEP必须使用1至3天之前的数据。GEP方法规定了用于预测三个潮汐站不同潮汐周期组成的不同方程。通过这些推导方程比较观察到的数据和预测值，可以发现 自相关函数（ACF）和部分自相关函数（PACF）图表说明，有效滞后的最大数量为3天，并且GEP必须使用1至3天之前的数据。GEP方法规定了用于预测三个潮汐站不同潮汐周期组成的不同方程。通过这些推导方程比较观察到的数据和预测值，可以发现R和均方根误差（RMSE）为0.867至0.944 m和0.058至0.149 m。