Papers by Dr. Mohamed Mattar
Agricultural Water Management, 2016
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Agricultural Water Management, 2016
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In this study, the irrigation water infiltration rate (IR) is defined by input variables in lingu... more In this study, the irrigation water infiltration rate (IR) is defined by input variables in linguistic terms using a fuzzy-logic approach. A fuzzy-logic model was developed using data collected from published data. The model was trained with three fuzzy membership functions: triangular ('trimf'), trapezoid ('trapmf'), and pi ('pimf'). The fuzzy system considered the number of irrigation events, applied water depth, polyacrylamide application rate, water application time, water electrical conductivity, soil surface slope, and soil texture components as input variables. The inputs were classified in terms of low, medium, and high levels. The output variable (i.e., IR) was rated in terms of five levels: very low, low, medium, high, and very high. Using statistical analysis, the values of IR resulting from the developed fuzzy-logic model were compared with the observations from the experiments. The results confirm that the agreement between the observations and predictive results was acceptable, except for fuzzy 'trimf'. The coefficient of determination provided the greatest value when using the 'trapmf' and 'pimf', with the value estimated for the 'pimf' slightly higher than that of 'trapmf'. Based on the results that were obtained, irrigation managers can use the fuzzy-logic approach to modify their field practices during the growing season to improve on-farm water management.
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Precisely determined evapotranspiration (ET) is necessary for maximization of water beneficiary u... more 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 (R2) 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.
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Springer, Sep 2016
This research investigates five reference evapotranspiration models (one combined model, one temp... more This research investigates five reference evapotranspiration models (one combined model, one temperature-based model, and three radiation-based models) under hyper-arid environmental conditions at the operational field level. These models were evaluated and calibrated using the weekly water balance of alfalfa by EnviroSCAN to calculate crop evapotranspiration (ET c). Calibration models were evaluated and validated using wheat and potatoes, respectively, on the basis of weekly water balance. Based on the results and discussion, the FAO-56 Penman-Monteith model proved to be superior in estimating ET c with a slight underestimation of 2 %. Meanwhile, the Hargreaves-Samani (HS) model (temperature-based) underestimated ET c by 20 % and the Priestley-Taylor (PT) and Makkink (MK) models (radiation-based) had similar performances underestimating by up to 35 % of the measured ET c. The Turc (TR) model had the lowest performance compared with other models, demonstrating values underestimated by up to 60 % of the measured ET c. Local calibration based on alfalfa evapotranspiration measurements was used to rectify these underestimations. The surprisingly good performance of the calibrated simple HS model, with a new coefficient 0.0029, demonstrated its favorable potential to improve irrigation scheduling. The MK and PT models were in third and fourth rank, respectively, reflecting minor differences between one another. The new coefficients obtained for the MK and PT models were 1.99 and 0.963, respectively. One important observation was that the calibrated TR model performed poorly, with an increase in its coefficient from 0.013 to 0.034 to account for hyper-arid environmental conditions; moreover, it required additional seasonal calibration to adequately improve its performance.
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Gene Expression Programming (GEP) was used to develop new mathematical equations for estimating ... more 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 PenmanMonteith 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 (HargreavesSamani, Irmak, JensenHaise and KimberlyPenman). The results showed that the statistical performance criteria values such as determination coefficients (R2) ranged from as low as 64.4% for GEPMOD1, where the only parameters included (maximum, minimum, and mean temperature and crop height), to as high as 95.5% for GEPMOD8 with which all climatic parameters included (maximum, minimum and mean temperature; maximum, minimum and mean humidity; solar radiation; wind speed; and crop height). More over, 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.
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Accurate estimations of reference evapotranspira-tion (ET ref) are extremely important for maximi... more Accurate estimations of reference evapotranspira-tion (ET ref) are extremely important for maximizing the beneficial use of water and hydrologic applications, particularly in arid and semiarid regions where water sources are so limited. The aim of this study is to develop mathematical models to calculate the daily ET ref using a gene expression programming (GEP) technique. Eight GEP models (GEP-MOD1–8) were developed from combinations of climatic variables. The Penman-Monteith equation was considered the reference method, with the reference plant height varying from 5 to 105 cm in 5-cm increments. Daily climatic variables collected from 13 meteorological stations, one station from every region within the Kingdom of Saudi Arabia, covered the 1980 to 2010 period. Of the available climatic data, 65 % was used in the training process for the eight developed GEP models, and 35 % was used in the testing process. The accuracy of the eight developed GEP models to estimate ET ref varied in significance depending on the climatic variables that were included. As more climatic parameters were input, the accuracy of the GEP model increased. For the testing process, the coefficient of determination (R 2) ranged from a low of 63.4 % for GEP-MOD1 to a high of 95.4 % for GEP-MOD8, and the root mean square error (RMSE) values ranged from 3.19 mm day −1 for GEP-MOD1 to 1.14 mm day −1 for GEP-MOD8. From the spatial evaluation, the values of RMSE ranged from 3.27 mm day − 1 for GEP-MOD1 to 1.21 mm day −1 for GEP-MOD8. In addition, the respective RMSE values resulting from GEP-MOD8 for plant heights of 50 and 12 cm were 0.75 and 0.96 cm. This implies that the developed GEP-MOD8 can be used for any value of the reference plant height ranging from 5 to 105 cm with insignificant errors. Interestingly, solar radiation had an almost insignificant effect on ET ref in the hyper-arid conditions. In contrast , wind speed and plant height had a large positive effect on increasing the accuracy of calculating ET ref .
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This study investigates the ability of gene expression programming (GEP) in modeling of the infil... more This study investigates the ability of gene expression programming (GEP) in modeling of the infiltrated water volume (Z) under furrow irrigation. Field data were collected in the literature study for modeling Z covering wide range of opportunity time. Five variables were used as input parameters; inflow rate (Q o), furrow length (L), waterfront advance time at the end of the furrow (T L), infiltration opportunity time (T o) and cross-sectional area of the inflow (A o). The following statistical parameters that coefficient of determination (R 2), overall index of the model performance (OI), root mean square errors (RMSE) and mean absolute errors (MAE) are used as comparing criteria for the evaluation of the models' performances. The best value of the statistical parameters which range in training, testing and validation processes as the following (R 2 = 95–97%; OI = 94–97%; RMSE = 0.013–0.009 m 3 m À1 ; and MAE = 0.011–0.007 m 3 m À1) implies that the GEP model provides an excellent fit for the measured data. A comparison is made between the estimates provided by the GEP and the two-point method. The comparison results reveal that the GEP models are superior to two-point method. Furthermore, the remarkable advantage of GEP was that it resulted in an explicit equation for the estimation of the Z under furrow irrigation.
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A soil box was used to investigate the water movement and soil water distribution around subsurfa... more A soil box was used to investigate the water movement and soil water distribution around subsurface drip laterals with two emitters in the presence of a thin layer of polyacrylamide (PAM). Sandy soil was uniformly packed into a soil box. The PAM was applied at a rate of 29.3 kg ha-1 as a 0.01% solution by spraying it directly onto the soil surface at the required depths. With an operating pressure of 150 kPa and no PAM layer, the water consumption with a dripper line depth of 0.15 m was 12% lower than with a dripper line depth of 0.10 m. The greatest improvement in soil water-holding capacity after the addition of a PAM layer (47%) was observed when the dripper line was placed at a depth of 0.15 m under an operating pressure of 100 kPa. With a dripper line depth of 0.15 m and an operating pressure of 100 kPa, the average moisture content in the vertical planes below and above the dripper line directly increased by about 7.4 and 20%, respectively, with PAM layers at 0.25 and 0.30 m depths when compared with the moisture content with no PAM layer. The combination of a dripper line depth of 0.15 m, a PAM layer depth of 0.30 m and an operating pressure of 100 kPa will achieve optimal water management.
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Artificial neural networks (ANNs) and gene expression programming (GEP) were compared to estimate... more Artificial neural networks (ANNs) and gene expression programming (GEP) were compared to estimate daily reference evapotranspiration (ETref) under arid conditions. The daily climatic variables were collected by 13 meteorological stations from 1980 to 2010. The ANN and GEP 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 centimetre. The developed models were spatially validated using climatic data from 1980 to2010 taken from another six meteorological stations. The results showed that the eight ETref models developed using the ANN technique were slightly more accurate than those developed using the GEP technique. The ANN models’ determination coefficients (R2) ranged from 67.6% to 99.8% and root mean square error (RMSE) values ranged from 0.20 to 2.95 mm d-1. The GEP models’ R2 values ranged from64.4% to 95.5% and RMSE values ranged from 1.13 to 3.1 mm d-1. Although the GEP models performed slightly worse than the ANN models, the GEP models used explicit equations.
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A mathematical model to forecast the solar still performance under hyper arid conditions was deve... more A mathematical model to forecast the solar still performance under hyper arid conditions was developed using artificial neural network technique. The developed model expressed by different forms, water productivity (MD), operational recovery ratio (ORR) and thermal efficiency (gth) requires ten input parameters. The input parameters included Julian day, ambient air temperature, relative humidity, wind speed, solar radiation, ultra violet index, temperature of the feed and brine water, and total dissolved solids of feed and brine water. The developed ANN model was trained, tested and validated based on measured data. The results showed that the coefficient of determination ranged from 0.991 to 0.99 and 0.94 to 0.98 for MD, ORR and gth during training and testing process, respectively. The average values of root mean-square error for all water were 0.04 L/m2/h, 2.60% and 3.41% for MD, ORR and gth respectively. Findings revealed that the model was effective and accurate in predicting solar still performance with insignificant errors.
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In this paper, we examine the discharge of labyrinth-channel emitters under different operating p... more 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 manufacturer’s 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.
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The aim of this study was to present an alternative means of procuring fresh water from low-quali... more The aim of this study was to present an alternative means of procuring fresh water from low-quality water sources to meet crop-water requirements (CWR) in greenhouses. A solar still was used in field experiments to desalinate three types of water: seawater, ground water and agricultural-drainage water. Three multiple linear regression models were derived, with an average coefficient of determination (R2) of 0.90 for the prediction of water-productivity capacity (MD). Two methods were used to estimate the CWR of greenhouses: the adapted Penman-Monteith (A-PM) method and the Fernandez (F) method. The R2 for the two methods was 0.95. The three water productivity measurements were compared with the water requirements throughout the year to determine the required area of the solar-desalination system. The results indicated that the A-PM method can be used to estimate the CWR of crops grown in greenhouses. Generally, MD exceeded CWR throughout the year, and the average MD of the water types was 4.79 L/m2/day. In addition, the average CWR values obtained using the A-PM and F methods were identical (1.88 L/m2/day). The water produced by 1 m2 of the solar-still system was also found to meet the CWR of about 2 m2 of greenhouse. As the system’s MD exceeds the CWR of a greenhouse, the proposed solar-desalination system is clearly able to meet greenhouse CWR.
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An artificial neural network (ANN) was developed for estimating the infiltrated water volume (Z) ... more An artificial neural network (ANN) was developed for estimating the infiltrated water volume (Z) under furrow irrigation. A feed-forward neural network using back-propagation training algorithm was developed for the prediction. Four variables were used as input parameters; inflow rate (Qo), furrow length (L),waterfront advance time at the end of the furrow (TL) and infiltration opportunity time (To). The Z was the one node in the output layer. The data used to develop the ANN model were taken from published experiments. The ANN model predicted Z over a wide range of the input variables with statistical analysis indicating that it can successfully predict Z with a high degree of accuracy. Performance evaluation criteria indicated that the ANN model was better than the two-point method using a volume balance model. Using testing and validation data sets to compare the ANN model with the two-point method shows that the two-point method had a mean coefficient of determination (R2) value that was about 3.6% less accurate than that from the ANN model. Also, the mean root mean square error (RMSE) value of 0.0135 m3 m−1 for the two-point method was almost double that of mean values for the ANN model. The relative errors of computed Z values for the ANN model were mostly around ±10%. Therefore, the ANN model is applicable to other soils and to different furrow irrigation hydraulics.
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Friction head loss equations and friction correction factors were evaluated and compared to field... more Friction head loss equations and friction correction factors were evaluated and compared to field observations collected from thirty
center pivots with laterals made of PVCs. The friction head loss equations include Darcy-Weisbach (D-W), Hazen-Williams (H-W), and Scobey, in addition to a proposed equation valid for smooth and rough pipe types and for all turbulent flow types. The proposed equation was developed by combining the equations of D-W and H-W, along with the multiple nonlinear regression technique. The friction correction factors were computed by using the typical Christiansen, modified Christiansen, Anwar, and Alazba formulae. The evaluation has been based on statistical error techniques with observed values as a reference. With the combination of modified Christiansen, Anwar, and Alazba formulae, the results revealed that the magnitudes of friction head loss calculated by using the D-W, H-W, and proposed equations were in agreement with field observations. The root mean square deviation (RMSD) values ranged from 1.6 to 1.7 m. As expected, and when the typical Christiansen friction correction factor was used with the D-W, H-W, and proposed equations, the results showed poor agreement between observed and computed friction head loss values. This was clearly reflected by the high RMSD values that ranged from 5.4 to 5.9 m. On the other hand, agreement occurred between observed friction head loss values and those calculated by using the Scobey equation, invalid for PVC pipe type, when combined with the typical Christiansen formula. This interesting finding led to improved results of the Scobey equation through a developed Cs coefficient suitably valid for PVC pipe type through analytically mathematical derivation; accordingly, the RMSD value dropped from approximately 8.6 to 1.6 m.
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The effects of water temperature and structural parameters of a labyrinth emitter on drip irrigat... more The effects of water temperature and structural parameters of a labyrinth emitter on drip irrigation hydraulic performance were investigated. The inside structural parameters of the trapezoidal labyrinth emitter include path width (W) and length (L), trapezoidal unit numbers (N), height (H), and spacing (S). Laboratory experiments were conducted using five different types of labyrinth-channel emitters (three non-pressure compensating and two pressure compensating emitters) commonly used for subsurface drip irrigation systems. The water temperature effect on the hydraulic characteristics at various operating pressures was recorded and a comparison was made to identify the most effective structural parameter on emitter performance. The pressure compensating emitter flow exponent (x) average was 0.014, while non-pressure compensating emitter’s values average was 0.456, indicating that the sensitivity of non-pressure compensating emitters to pressure variation is an obvious characteristic (p < 0.001) of this type of emitters. The effects of water temperature on emitter flow rate were insignificant (p > 0.05) at various operating pressures, where the flow rate index values for emitters were around one. The effects of water temperature on manufacturer’s coefficient of variation (CV) values for all emitters were insignificant (p > 0.05). The CV values of the non-pressure compensating emitters were lower than those of pressure compensating emitters. This is typical for most compensating models because they are manufactured with more elements than non-compensating emitters are. The results of regression analysis indicate that N and H are the essential factors (p < 0.001) to affect the hydraulic performance.
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Papers by Dr. Mohamed Mattar
center pivots with laterals made of PVCs. The friction head loss equations include Darcy-Weisbach (D-W), Hazen-Williams (H-W), and Scobey, in addition to a proposed equation valid for smooth and rough pipe types and for all turbulent flow types. The proposed equation was developed by combining the equations of D-W and H-W, along with the multiple nonlinear regression technique. The friction correction factors were computed by using the typical Christiansen, modified Christiansen, Anwar, and Alazba formulae. The evaluation has been based on statistical error techniques with observed values as a reference. With the combination of modified Christiansen, Anwar, and Alazba formulae, the results revealed that the magnitudes of friction head loss calculated by using the D-W, H-W, and proposed equations were in agreement with field observations. The root mean square deviation (RMSD) values ranged from 1.6 to 1.7 m. As expected, and when the typical Christiansen friction correction factor was used with the D-W, H-W, and proposed equations, the results showed poor agreement between observed and computed friction head loss values. This was clearly reflected by the high RMSD values that ranged from 5.4 to 5.9 m. On the other hand, agreement occurred between observed friction head loss values and those calculated by using the Scobey equation, invalid for PVC pipe type, when combined with the typical Christiansen formula. This interesting finding led to improved results of the Scobey equation through a developed Cs coefficient suitably valid for PVC pipe type through analytically mathematical derivation; accordingly, the RMSD value dropped from approximately 8.6 to 1.6 m.
center pivots with laterals made of PVCs. The friction head loss equations include Darcy-Weisbach (D-W), Hazen-Williams (H-W), and Scobey, in addition to a proposed equation valid for smooth and rough pipe types and for all turbulent flow types. The proposed equation was developed by combining the equations of D-W and H-W, along with the multiple nonlinear regression technique. The friction correction factors were computed by using the typical Christiansen, modified Christiansen, Anwar, and Alazba formulae. The evaluation has been based on statistical error techniques with observed values as a reference. With the combination of modified Christiansen, Anwar, and Alazba formulae, the results revealed that the magnitudes of friction head loss calculated by using the D-W, H-W, and proposed equations were in agreement with field observations. The root mean square deviation (RMSD) values ranged from 1.6 to 1.7 m. As expected, and when the typical Christiansen friction correction factor was used with the D-W, H-W, and proposed equations, the results showed poor agreement between observed and computed friction head loss values. This was clearly reflected by the high RMSD values that ranged from 5.4 to 5.9 m. On the other hand, agreement occurred between observed friction head loss values and those calculated by using the Scobey equation, invalid for PVC pipe type, when combined with the typical Christiansen formula. This interesting finding led to improved results of the Scobey equation through a developed Cs coefficient suitably valid for PVC pipe type through analytically mathematical derivation; accordingly, the RMSD value dropped from approximately 8.6 to 1.6 m.