Land Use Land Cover Change (LULCC) and urban growth prediction and analysis are two of the best methods that can help decision-makers for better sustainable management and planning of socioeconomic development in the countries. In the present paper, the growth of urban land use was analyzed and predicted in all districts of the El Baha region (Kingdom of Saudi Arabia) based on high-resolution Landsat, 5, 7, and 8 satellite imagery during the period of study between 1985–2021. Using remote sensing techniques, the LULCC were obtained based on the maximum likelihood classification (MLC), where the geographic information system (GIS) had been used for mapping LULCC classes. Furthermore, Markov cellular automata (MCA) in Idrisi TerrSet was applied for assessing the future growth of urban land use between 2021–2047. The findings of the LULCC analysis based on the MLC indicate great socioeconomic development during the study period and that the urban expansion was at the expense of rangeland, forest and shrubland, and barren land and sand areas, with the contribution of each in the built-up area estimated to be around 9.1% (179.7 km²), 33.4% (656.3 km²) and 57.5% (1131.5 km²), respectively. The simulation of the future LULCC period 2021–2047 revealed a loss in rangeland, forest and shrubland, and barren land and sand by 565, 144 and 105 km², respectively, where rangeland is the most influenced, its land cover will decrease from 4002 to 3437 km². From the obtained results based on MCA, urban growth is predicted to be large and it is estimated at around 2607 km² until the year 2047 with a net increase of 811 km². The results obtained from this study may provide information to help decision-makers to implement efficient practices for future planning and management of the growth of urban land use, especially Saudi vision 2030.

Saudi Arabia is a developing country that is experiencing a rapid growth in its population and level of urbanisation. Higher education (HE) in the country has developed rapidly over the last ten years, and it is still moving through numerous major reforms. Largely, the concept of sustainability has not yet been formally adopted in public institutions in a way that could sufficiently remedy the range of activities that currently impact negatively on the environment. The central aim of this paper is to examine the extent to which planning and action for sustainability is currently being taken on university campuses in Saudi Arabia, and to review the opportunities and challenges for encouraging and enabling further progress to this end. The research that the paper draws on specifically investigated the influence of decision makers’ personal knowledge and perceptions within Facilities and Project Management (FPM) departments at selected Saudi universities, and the constraints faced by FPM decision makers with regard to the promotion of sustainability on campus. This exploration was supported by the development of a theoretical framework that draws on rational choice theory (RCT). The research revealed mixed levels of prevailing knowledge and awareness towards sustainability among FPM decision makers within the case study university campuses. Cost notably came across as a dominant influence on FPM decision makers’ choices and decisions, and it undoubtedly plays an important role in shaping the decision-making process alongside other key organisational factors. A number of barriers facing the incorporation of sustainability emerged with clarity, such as the lack of supportive leadership, the lack of sustainability knowledge and awareness among senior management and an absence of sustainability-related legislation policy or strategic direction in the HEIs concerned.

Among the Saudi Vision 2030 programs is the Green Saudi Initiative, which aims to protect the environment, energy conversion, and sustainability projects to build a sustainable future. In the present paper, Green Infrastructures (GI) were assessed, analyzed, and mapped using GIS and Analytic Hierarchy Process-based-Multi-Criteria Decision-Making Method (AHP-MCDM). Ten criteria were selected to elaborate the GI suitability map (DEM, slope, topographic position index, rainfall, distance to the water lines, topographic wetness index, distance to the road, wind speed, housing income group high (high-income people) map, employment in the agricultural sector, and land use land change). The results revealed four classes of suitability: Poor, Fair, Good, and Excellent. The “Excellent” area for GI planning was estimated at 983 km² (9%), whereas the “Good” area covered 36% (3987 km²). The excellent and good areas for GI were more localized in the central part of the Al Baha region in the areas of Al Bahah, Elmandaq, Alatawlah, and the central part of Buljurshi. According to the obtained results, the southern part of the study is not suitable for GI planning; this is explained by the large area of barren land and sand. The results obtained by this research may help managers and decision-makers in future planning for GI areas in the Al Baha region.

Land cover assessment plays a vital role in both current and future planning and use of natural resources for sustainable development for any country. For the good practice of the vision 2030 in Al Baha region (south-western Saudi Arabia), land cover was assessed, classified, and analyzed using remote sensing databases and time series analysis combined with spatial analysis in geographic information system (GIS) based on high-resolution Landsat 8 OLI, Sentinel-2 satellite imagery between the period of study 2017/2018 and 2021/2022. Based on both an accuracy assessment and kappa test, the results indicate that Esri Sentinel-2 imagery gives the highest performance compared to Landsat 8 OLI with accuracy and kappa test equal to 87% and 84%, respectively. On the other hand, the land cover classification revealed that the large area of water bodies is localized on Alaqiq (1.45 km²), Baljurish (0.94 km²), and Elmelkhwah (1.57 km²). Furthermore, the built area of the Al Baha region between 2017 and 2021 was estimated to increase by 144 km² (from 516.5 to 661.07 km²), which is especially significant for the Qelwah district (from 16.97 to 44.16 km²) which demonstrated a decrease in bare ground area of approximately 320 km². The crop lands have been increased by 162.74% in the 2017–2021 period from 10.39 km² to 16.90 km², particularly at Qelwah and Elmelkhwah. Finally, the results obtained by this research can help decision-makers and managers for better natural resources management in the Al Baha region.

Examining the factors contributing to work zone crashes and implementing measures to reduce their occurrence can significantly improve road safety. In this research, we utilized the explainable boosting machine (EBM), a modern glass-box machine learning (ML) model, to categorize and predict work zone-related crashes and to interpret the various contributing factors. The issue of data imbalance was also addressed by utilizing work zone crash data from the state of New Jersey, comprising data collected over the course of two years (2017 and 2018) and applying data augmentation strategies such synthetic minority over-sampling technique (SMOTE), borderline-SMOTE, and SVM-SMOTE. The EBM model was trained using augmented data and Bayesian optimization for hyperparameter tuning. The performance of the EBM model was evaluated and compared to black-box ML models such as combined kernel and tree boosting (KTBoost, python 3.7.1 and KTboost package version 0.2.2), light gradient boosting machine (LightGBM version 3.2.1), and extreme gradient boosting (XGBoost version 1.7.6). The EBM model, using borderline-SMOTE-treated data, demonstrated greater efficacy with respect to precision (81.37%), recall (82.53%), geometric mean (75.39%), and Matthews correlation coefficient (0.43). The EBM model also allows for an in-depth evaluation of single and pairwise factor interactions in predicting work zone-related crash severity. It examines both global and local perspectives, and assists in assessing the influence of various factors.

Introduction: In recent years, campus sustainability has gained global prominence, with a focus on sustainability initiatives and environmental activities within university campuses. This study aims to explore the factors contributing to the expansion of sustainability initiatives in Saudi Arabian university campuses.Methods: The research utilizes the Interpretive Structural Model (ISM) as a framework for public universities. A literature review incorporating rational choice theory (RCT) and organizational change theory (OCT) is conducted to identify variables relevant to campus sustainability. The hierarchical interrelationships among these variables are examined, and the Matrice d’Impacts Croises-Multiplication Appliqué and Classment Analysis (MICMAC) categorize the study variables. Associations between the variables are validated through expert consultations from selected case study institutions.Results: The findings highlight the significance of organizational structure and decision-making processes in determining the prevalence of other sustainability-related variables. Organizational structure and decision-making processes are recognized as key factors exerting significant influence over other variables. Additionally, leadership is categorized as a linkage variable, representing a strong relationship within the hierarchical model.Discussion: The study acknowledges the challenges in implementing sustainability initiatives, including conflicts in strategic planning, absence of campus sustainability policies and regulations, financial barriers, and lack of support from leadership. The study offers valuable insights and recommendations for stakeholders involved in sustainability initiatives within higher education institutions, particularly university leaders and decision makers in facility and project management departments (F&PM). By addressing the organizational and decision-based factors, effective integration of sustainability practices into university planning and activities can be achieved. This research contributes to filling the knowledge gap regarding the factors influencing the implementation of sustainability in Saudi Arabian higher education institutions (HEIs). The study emphasizes the importance of addressing organizational structure, decision-making processes, and leadership in order to successfully implement sustainability practices on university campuses.