JWSS - Journal of Water and Soil Science

Rainfall may be considered as the most important source of drinking water and watering land in different areas all over the world. Therefore, simulation and estimation of the hydrological phenomenon is of paramount importance. In this study, for the first time, the long-term rainfall in Rasht city was simulated using an optimum hybrid artificial intelligence (AI) model over a 62 year period from 1956 to 2017. The gene expression programming (GEP) and wavelet transform (WT) were combined to develop the hybrid AI model (WGEP). Firstly, the most effective lags of time series data were identified by means of the autocorrelation function (ACF); then eight various GEP and WGEP models were defined. Next, the GEP models were analyzed and the superior GEP model as well as the most influenced lags was detected. For instance, the variance accounting for (VAF), correlation coefficient (R) and scatter index (SI) for the superior GEP model was calculated to be 0.765, 0.508 and 0.709, respectively. Additionally, lags (t-1), (t-2), (t-3) and (t-12) were the most influenced. Then, the different mother wavelets were examined, indicating that the demy mother wavelet was the most optimal one. Moreover, analyzing the numerical simulations showed that the mother wavelet enhanced the performance of the GEP model significantly. For example, the VAF index for the superior WGEP model was increased almost three times after using the mother wavelet. Furthermore, the R and MARE statistical indices for the WGEP model were computed to be 0.935 and 0.862, respectively.

Nowadays, the prediction of river discharge is one of the important issues in hydrology and water resources; the results of daily river discharge pattern could be used in the management of water resources and hydraulic structures and flood prediction. In this research, Gene Expression Programming (GEP), parametric Linear Regression (LR), parametric Nonlinear Regression (NLR) and non-parametric K- Nearest Neighbor (K-NN) were used to predict the average daily discharge of Karun River in Mollasani hydrometric station for the statistical period of 1967-2017. Different combinations of the recorded data were used as the input pattern to predict the mean daily river discharge. The obtained esults  indicated that GEP, with R²= 0.827, RMSE= 59.45 and MAE= 26.64, had a  better performance, as compared to LR, NLR and K-NN methods, at the  validation stage for daily Karun River discharge prediction with 5-day lag, at the Mollasani station. Also, the performance of the models in the maximum discharge prediction showed that all models underestimated the flow discharge in most cases. 

The FAO Penman-Monteith is a baseline method to estimate reference evapotranspiration. In many cases, it is difficult to access all data, so replacing simpler models with ‎lower input data and appropriate accuracy is necessary. ‎ The purpose of this study is to investigate the capability of the experimental ‎models, gene expression programming, stepwise regression, and Bayesian network in estimating ‎reference evapotranspiration.‎ In this research, daily information of the Boroujerd synoptic station in the period of 1996 -2017 was used as model inputs. ‎Based on the correlation between input and output parameters, six input patterns were ‎determined for modeling. The results showed that the Kimberly-Penman model has the ‎best performance among the experimental models.‎ Gene expression programming with fourth pattern ‎‎and Default Model Operators (R² = 0.98 and RMSE = 0.9), Bayesian Network with sixth pattern (R²=0.91 and RMSE = 1.01), and stepwise regression with sixth pattern have the most accurate patterns at R² = 0.91 and RMSE = 0.9 in the ‎training stage.‎ Comparison of the performance of the three models showed that the gene expression ‎programming model was superior to the other two models with the Average Absolute Relative Error (AARE) of 0.12 and the Mean Ratio (MR) of 0.94.‎ The results showed that gene expression programming had an acceptable ability to estimate ‎reference evapotranspiration under the weather conditions of Boroujerd and could be introduced as a ‎suitable model.‎