Browsing by Author "Alshehri, Fahad"

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Advancing Depth-Storage-Discharge Modeling in Regional Hydrology
(University of South Florida, 2024-04-26) Alshehri, Fahad; Ross, Mark
This dissertation presents the development of an innovative approach to populating rating characteristics and supporting hydrologic modeling, designed to simplify complex real-world hydrological systems and accurately estimate their responses to rainfall, runoff, baseflow and evaporation stresses. The core of this research addresses the challenges inherent in characterizing hydrography elements in hydrologic modeling, particularly in regions lacking comprehensive stream reach survey data, flow and stage. This issue is pronounced in areas with extensive wetland hydrography, where traditional modeling requires intensive manual calibration, and course rating data that are often unavailable. To overcome these challenges, this study introduces a novel procedure that leverages Geographic Information System (GIS) coverages and limited streamflow gauging station data to characterize and define rating conditions for hydrologic models. This methodology employs non-dimensionalizing techniques to enable effective (and not overparameterized) modeling of large, poorly gauged areas. Key to this approach is the reduced calibration required, significantly reducing the labor-intensive aspect of traditional methods. Results from applying this procedure on a large regional basin in W-C Florida demonstrate efficacy and accuracy for modeling reach water levels, and discharge behavior in large modestly gauged stream systems. This area exhibits sizeable fluxes of both event runoff and sustained groundwater contributions. The findings suggest substantial advancements in capability, alongside time savings and effort, in construction and calibration of large regional hydrologic models especially where significant wetland areas are present, presenting what is believed to be a significant contribution to the field of hydrologic modeling. This research not only provides a practical solution to a longstanding modeling problem but also suggests future research needs and possible advancements for the future.
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Saudi Primary Teachers' Perceptions of Artificial Intelligence in Education: A Qualitative Investigation Through the TPACK-C Framework
(Saudi Digital Library, 2025-06-28) Alshehri, Fahad; Marc, Pruyn
Saudi Arabia's Vision 2030 identifies education as the principal driver of economic diversification and situates the Human Capability Development Program at the core of workforce preparation. Notwithstanding these ambitions, the 2018 PISA cycle showed that more than 50 percent of Saudi learners failed to attain minimum reading proficiency. The COVID-19 crisis subsequently demonstrated the system's capacity for rapid digital adaptation, characterised by agility and innovation. In this milieu, artificial intelligence has shifted from peripheral curiosity to policy imperative. No published study has yet employed the Technological Pedagogical Content Knowledge-Contextualised (TPACK-C) model enriched with Islamic constructs to investigate Saudi primary classrooms. This qualitative study explores five primary teachers' AI perceptions through TPACK-C, TAM, and SDT frameworks. Semi-structured Arabic interviews with teachers from diverse infrastructural contexts were analyzed using reflexive thematic analysis, revealing six key themes: instructional-efficiency catalyst, the infrastructure divide, variable student digital readiness, professional identity re-negotiation, fragmented professional learning, and the necessity of ethical-cultural safeguarding. Findings reveal a novel Threshold-Trajectory Model requiring sequential progression through digital-infrastructure, competency development, and ethical-cultural alignment phases. Results extend TPACK-C with Student Technological Knowledge (STK) and a culturally specific Ethical Knowledge domain, while providing actionable recommendations supporting Vision 2030 objectives.
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