Mohamed A. Mattar

Artificial neural networks for estimating the hydraulic performance of labyrinth-channel emitters

The emitter flow variation (qvar) and manufacturers coefficient of variation (CV) were modeling.Artificial neural network (ANN) was developed for estimating of the emitter hydraulic performance.Statistical criteria indicated that the ANN model was better than multiple linear regression model.Using the ANN model provides qvar and CV values with high accuracy. In this paper, we examine the discharge of labyrinth-channel emitters under different operating pressures (P) and water temperatures (T). An artificial neural network (ANN) and multiple linear regression (MLR) model are developed for the emitter flow variation (qvar) and the manufacturers coefficient of variation (CV). As well as P and T, the structural parameters of the labyrinth emitter are considered as independent variables. The ANN results demonstrate that a feed-forward back-propagation network with five input neurons and 14 neurons in the hidden layer successfully model qvar and CV. The trapezoidal unit spacing and path length of the labyrinth emitter are found to be insignificant. In our ANN model, we use a hyperbolic tangent as the activation function in the hidden layer and the output layer. Statistical criteria indicate that the ANN is better at predicting the hydraulic performance of the labyrinth emitters than MLR. The root mean square errors for qvar and CV are 1.0497 and 0.0044, respectively, for the ANN model, and 2.0703 and 0.0107, respectively, for the MLR model using a test dataset. The relatively low errors obtained by the ANN approach lead to high model predictability and are feasible for modeling the hydraulic performance of labyrinth emitters.

Comparison between gene expression programming and traditional models for estimating evapotranspiration under hyper arid Conditions

Gene Expression Programming (GEP) was used to develop new mathematical equations for estimating daily reference evapotranspiration (ETref) for the Kingdom of Saudi Arabia. The daily climatic variables were collected by 13 meteorological stations from 1980 to 2010. The GEP models were trained on 65% of the climatic data and tested using the remaining 35%. The generalised Penman-Monteith model was used as a reference target for evapotranspiration (ET) values, with hc varies from 5 to 105 cm with increment of a centimetre. Eight GEP models have been compared with four locally calibrated traditional models (Hargreaves-Samani, Irmak, Jensen-Haise and Kimberly-Penman). The results showed that the statistical performance criteria values such as determination coefficients (R2) ranged from as low as 64.4% for GEP-MOD1, where the only parameters included (maximum, minimum, and mean temperature and crop height), to as high as 95.5% for GEP-MOD8 with which all climatic parameters included (maximum, minimum and mean temperature; maximum, minimum and mean humidity; solar radiation; wind speed; and crop height). Moreover, an interesting founded result is that the solar radiation has almost no effect on ETref under the hyper arid conditions. In contrast, the wind speed and plant height have a great positive impact in increasing the accuracy of calculating ETref. Furthermore, eight GEP models have obtained better results than the locally calibrated traditional ETref equations.

MODELLING DAILY EVAPOTRANSPIRATION USING ARTIFICIAL NEURAL NETWORKS UNDER HYPER ARID CONDITIONS

Precisely determined evapotranspiration (ET) is necessary for maximization of water beneficiary use and hydrologic applications, particularly in arid and semiarid regions where water source is so limited, such as Saudi Arabia. Evapotranspiration is a complex, nonlinear process. However, data driven techniques can be used model it without requiring a complete understanding of the physics involved. Therefore, the Artificial Neural Networks (ANN) technique was used to estimate the daily reference evapotranspiration (ETref). Eight combinations of eight climatic parameters and crop height were used as input. The daily climatic variables were collected by 13 meteorological stations from 1980 to 2010. The ANN models were trained on 65% of the climatic data and tested using the remaining 35%. The generalised Penman-Monteith (PMG) model was used as a reference target for evapotranspiration values, with hc varies from 5 to 105 cm with increment of a centimeter. The developed models were spatially validated using climatic data from 1980 to 2010 taken from another six meteorological stations. The results showed that the eight ETref models developed using the ANN technique to estimate ETref varies in significance depending on the climatic variables included. The more input climatic parameters included, the more accurate the ANN model is. The statistical performance criteria values such as determination coefficients (R 2 ) ranged from as low as 67.6% for ANN-MOD1, where air temperature is the only climatic parameter included, to as high as 99.8% for ANN-MOD8 with which all climatic parameters included. Furthermore, an interesting founded result is that the solar radiation has almost no effect on ETref under the hyper arid conditions. In contrast, the wind speed and plant height have a great positive impact in increasing the accuracy of calculating the daily reference evapotranspiration.

Gene expression programming approach for modeling the hydraulic performance of labyrinth-channel emitters

GEP was developed to predict of the emitter hydraulic performance qvar and CVm.The GEPwithout L model was better than GEPwithout S model for qvar.The GEPwithout S model was better than GEPwithout L model for CVm.The predicted non-pressure-compensating emitters qvar and CVm were more accurate.The GEP approach leads to high model predictability. The different hydraulic measures of emitter flow variation (qvar) and manufacturers coefficient of variation (CVm) at different operating pressure (P) and water temperature (T) were determined by measuring the discharge of different labyrinth-channel emitters. Gene expression programming (GEP) was used to model and predict qvar and CVm of the labyrinth emitters. The structural parameters of each labyrinth emitter [namely, trapezoidal unit number (N), height (H), and spacing (S), and path width (W) and length (L)] as well asP and T were considered as independent variables. The accuracy of GEP models was evaluated by their coefficient of determination (R2), root-mean-square error (RMSE), overall index of model performance (OI), and mean absolute error (MAE). Results of GEP applications established that L and S were the least important variables affecting qvar and CVm, respectively, while N and H were the most important variables. For qvar, the GEPwithout L model gave higher R2 and OI and lower RMSE and MAE than those of the GEPwithout S model. Conversely, for CVm, R2 and OI of the GEPwithout L model were lower and its RMSE and MAE were higher than the corresponding parameters of the GEPwithout S model. Overall, our results indicated that the performance of the developed GEP models were better at predicting qvar and CVm for non-pressure-compensating emitters than pressure-compensating ones. The GEP approach can be a good tool to predict the hydraulic performance of labyrinth emitters.