Accurate estimation of evapotranspiration has crucial importance in arid regions like Egypt, which suffers from the scarcity of precipitation and water shortages. This study provides an investigation of the performance of 31 widely used empirical equations and 20 models developed using five artificial intelligence (AI) algorithms to estimate reference evapotranspiration (ETo) to generate gridded high-resolution daily ETo estimates over Egypt. The AI algorithms include support vector machine-radial basis function (SVM-RBF), random forest (RF), group method of data handling neural network (GMDH-NN), multivariate adaptive regression splines (MARS), as well as Dynamic Evolving Neural Fuzzy Interference System (DENFIS). Daily observations records of 41 stations distributed over Egypt were used to calculate ETo using FAO56 Penman-Monteith equation as a reference estimate. The multi-parameter Kling-Gupta efficiency (KGE) metric was used as an evaluation metric for its robustness in representing different statistical error/agreement characteristics in a single value. By category, the empirical equations based on radiation performed better in replication FAO56-PM followed by temperature-and mass-transfer-based ones. Ritchie equation was found to be the best overall in Egypt (median KGE 0.75) followed by Caprio (median KGE 0.65), and Penman (median KGE 0.52) equations based on station-wise ranking. On the other hand, the RF model, having maximum and minimum temperatures, wind speed, and relative humidity as predictors, outperformed other AI algorithms. The generated 0.10°×0.10° daily estimates of ETo enabled the detection of a significant increase of 0.12-0.16 mm/decade in the agricultural-dependent Nile Delta using the modified Mann Kendall test and Sen's slope estimator.
Accurate estimation of evapotranspiration has crucial importance in arid regions like Egypt, which suffers from the scarcity of precipitation and water shortages. This study provides an investigation of the performance of 31 widely used empirical equations and 20 models developed using five artificial intelligence (AI) algorithms to estimate reference evapotranspiration (ET o ) to generate gridded high-resolution daily ET o estimates over Egypt. The AI algorithms include support vector machine-radial basis function (SVM-RBF), random forest (RF), group method of data handling neural network (GMDH-NN), multivariate adaptive regression splines (MARS), as well as Dynamic Evolving Neural Fuzzy Interference System (DENFIS). Daily observations records of 41 stations distributed over Egypt were used to calculate ET o using FAO56 Penman-Monteith equation as a reference estimate. The multi-parameter Kling-Gupta efficiency (KGE) metric was used as an evaluation metric for its robustness in representing different statistical error/agreement characteristics in a single value. By category, the empirical equations based on radiation performed better in replication FAO56-PM followed by temperature- and mass-transfer-based ones. Ritchie equation was found to be the best overall in Egypt (median KGE 0.75) followed by Caprio (median KGE 0.65), and Penman (median KGE 0.52) equations based on station-wise ranking. On the other hand, the RF model, having maximum and minimum temperatures, wind speed, and relative humidity as predictors, outperformed other AI algorithms. The generated 0.10°×0.10° daily estimates of ET o enabled the detection of a significant increase of 0.12-0.16 mm/decade in the agricultural-dependent Nile Delta using the modified Mann Kendall test and Sen’s slope estimator.
This paper numerically investigates the flexural response of concrete beams reinforced with steel and four types of Fiber-Reinforced Polymers (FRP), i.e., Carbon FRP (CFRP), Glass FRP (GFRP), Aramid FRP (AFRP), and Basalt FRP (BFRP). The flexural responses of forty beams with two boundary conditions (simply supported and over-hanging beams) were determined using ABAQUS. Subsequently, the finite element models were validated using experimental results. Eventually, the impact of the reinforcement ratios ranging between 0.15% and 0.60% on the flexural capacity, crack pattern, and fracture energy were investigated for all beams. The results revealed that, for the low reinforcement ratios, the flexural performance of CFRP significantly surpassed that of steel and other FRP types. As the reinforcement ratio reached 0.60%, the steel bars exhibited the best flexural performance.
Compacted clays are commonly utilized as landfill liners due to their impermeable properties, however, the availability of natural clay soil may be difficult or prohibitively expensive in some regions thanks to the expense of transportation, the local soil availability or regulation. As an alternative hydraulic barrier, a compacted mixture of sand with a low percentage of bentonite (6% to 14%) was used. The purpose of this research is to investigate the applicability of bentonite available in the Egyptian market for controlling the hydraulic conductivity of sand-bentonite mixtures to be used in landfill liners. The study was divided into two phases: first, laboratory tests were conducted to identify the optimum bentonite percentage and bentonite types, using local commercial bentonite products. Then, in two distinct landfill liners layers, full-scale measurements were obtained. The experimental program demonstrated that Bentonite produced and found in the Egyptian market can be utilized in landfill liners with an acceptable quality control procedure to prevent any outcome deviation.
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