Surface runoff is considered one of the most significant water resources in arid and semi-arid environments. Estimating the volume of surface runoff through the application of Geographic Information Systems (GIS), along with maps detailing the geomorphological and geological characteristics of the Wadi Haran basin, is an essential process. This estimation is supported by mathematical equations based on the Soil Conservation Service Curve Number method (SCS-CN), which is among the most widely used approaches for calculating runoff volume. The study area, Wadi Haran basin, covers approximately 1,895 km². The Curve Number (CN) was found to be 92, with runoff volume (QV) values ranging between 582.71 m³ and 12.85 m³. The total surface runoff was calculated to be 198.2 m³. These results confirm the presence of significant surface runoff in the northern and northeastern parts of the basin. This is primarily attributed to the wide basin area and the presence of hard, low-permeability geomorphological soil characteristics that contribute to water accumulation and surface flow, rather than infiltration into the soil. Conversely, the southwestern part of the basin, characterised by higher-permeability geomorphological features, exhibits lower surface runoff. Overall, the basin is characterised by increased surface runoff rates, particularly in the northern and northeastern sections, due to the broader basin area and low soil permeability compared to the southern and southwestern areas.
Advanced strategies for production forecasting, operational optimization, and decision-making enhancement have been employed through reservoir management and machine learning (ML) techniques. A hybrid model is established to predict future gas output in a gas reservoir through historical production data, including reservoir pressure, cumulative gas production, and cumulative water production for 67 months. The procedure starts with data preprocessing and applies seasonal exponential smoothing (SES) to capture seasonality and trends in production data, while an Artificial Neural Network (ANN) captures complicated spatiotemporal connections. The history replication in the models is quantified for accuracy through metric keys such as m
... Show MoreIn this research, the performance of asphalt mixtures modified with polyethylene polymer (PE) by adding 2%, 4%, and 6% percentages was evaluated. Two kinds of PE are employed: Low-Density PE (LDPE) and High-Density PE (HDPE). The semi-wet mixing technique (SWM) was conducted to avoid stability issue for PE-modified binder during storage condition. Many experimental tests were conducted to evaluate the ability of these mixtures to withstand the effects of loads and moisture. The hardness index of these mixtures was also measured to determine their resistance to the effects of high temperatures without causing permanent deformations. The results showed that adding PE led to a remarkable enhancement in the performance of PE-modified mixtures.
... Show MoreThis work investigates the use of hydrogenated amorphous silicon (a-Si:H) as a high-refractive-index material for quarter-wave distributed Bragg reflectors (DBRs) in photonic applications. In comparison to Si3N4, a-Si:H enables enhanced optical confinement, broader omnidirectional reflectance, and improved figures of merit, including higher Purcell and quality factors, while minimizing mirror complexity. To evaluate the practical impact of these advantages, a theoretical comparison is conducted between Fabry–Pérot cavities based on a-Si:H/SiO2 and Si3N4/SiO2 DBRs, examining resonance shifts as functions of cavity refractive index (1.0–3.0) and temperature (0–250 °C). The numerical results indicate that Si3N4/SiO2 planar Bragg caviti
... Show MoreThis work investigates the impacts of eccentric-inclined load on ring footing performance resting on treated and untreated weak sandy soil, and due to the reduction in the footing carrying capacity due to the combinations of eccentrically-inclined load, the geogrid was used as reinforcement material. Ring radius ratio and reinforcement depth ratio parameters were investigated. Test outcomes showed that the carrying capacity of the footing decreases with the increment in the eccentric-inclined load and footing radius ratio. Furthermore, footing tilt and horizontal displacement increase with increasing the eccentricity and inclination angle, respectively. At the same time, the increment in the horizontal displacement due t
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