Sea level change, a consequence of climate change, poses a global threat with escalating impacts on coastal regions. Since 1880, global mean sea level has risen by 8–9 inches (21–24 cm), reaching a record high in 2021. Projections by NOAA suggest an additional 10–12-inch increase by 2050. This paper explores research methodologies for studying sea level change, focusing on Geographic Information System (GIS) techniques. GIS has become a powerful tool in sea level change research, allowing the integration of spatial data, coastal process modeling, and impact assessment. This paper sets the link with sustainability and reviews key factors influencing sea level change, such as thermal expansion and ice-mass loss, and examines how GIS is applied. It also highlights the importance of using different scenarios, like Representative Concentration Pathways (RCP), for accurate predictions. The paper discusses data sources, index variables like the Coastal Vulnerability Index, and GIS solutions for modeling sea level rise impacts. By synthesizing findings from previous research, it contributes to a better understanding of GIS methodologies in sea level change studies. This knowledge aids policymakers and researchers in developing strategies to address sea level change challenges and enhance coastal resilience. Furthermore, global analysis highlights the pivotal roles of the United States and China in sea level change (SLC) and GIS research. In the Gulf Cooperation Council (GCC) region, rising temperatures have substantial impacts on local sea levels and extreme weather events, particularly affecting vulnerable coastal areas.

Flash floods pose serious risks to human life and infrastructure, leading to significant economic losses. While traditional conceptual models have long been used for runoff estimation, recent advancements in artificial intelligence have introduced machine learning and deep learning models for more accurate predictions. This study presents a deep learning framework that integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Neural Ordinary Differential Equations (Neural ODEs) to enhance precipitation-induced runoff forecasting. A six-year dataset (2016–2022) from Al Ain, United Arab Emirates (UAE), was employed for model training, with validation conducted using data from a severe April 2024 flash flood. The proposed framework was compared against standalone CNN, RNN, and Neural ODE models to evaluate its predictive performance. Results show that the combination of the CNN’s feature extraction, the RNN’s temporal analysis, and the Neural ODE’s continuous-time modeling achieves superior accuracy, with an R² value of 0.98, RMSE = 2.87 × 10⁶, MAE = 1.13 × 10⁶, and PBIAS of −8.38. These findings highlight the model’s ability to effectively capture complex hydrological dynamics. The framework provides a valuable tool for improving flash-flood forecasting and water resource management, especially in arid regions like the UAE. Future work may explore its application in different climates and integration with real-time monitoring systems.

As universities strive to create sustainable and comfortable learning environments, understanding the factors that influence student well-being is crucial for promoting good health and well-being (SDG 3) and fostering sustainable communities (SDG 11). This study, conducted at a female campus in the UAE, investigates the impact of various external factors on students’ psychological perceptions. Specifically, it examines how abaya color, landscape settings, and time of day affect body fatigue, eye fatigue, and thermal discomfort, providing valuable insights for campus planning and design. Using GrADS and an FLIR thermal camera, this research analyzed temperature, humidity, and surface temperatures. The Kruskal–Wallis test and Don Bonferroni pairwise comparisons were employed to assess the impact of conditions on psychological perceptions. The results indicate that abaya color insignificantly affected perceptions in summer, but light brown was preferred in spring. Landscape sites influenced eye fatigue and skin dryness in summer, favoring shaded areas. The time of day affected body heat, skin dryness, and thermal discomfort, with greater discomfort in summer afternoons. These findings offer valuable insights for campus planning, particularly in hot summer months, promoting students’ psychological well-being (SDG 3) and sustainable campus communities (SDG 11).

Purpose: The present study was conducted at the female campus of the United Arab Emirates University (UAEU), with a specific emphasis on the outside environment. This study aims to explore the influence of external conditions, including AC (AC), Time of Day (TD) variations, and Landscape Sites (LS) differences, on changes in physiological temperatures. Specifically, the focus is on understanding the dynamics of skin and abaya temperature (AT) when exposed to transient thermal environments, considering factors such as AC, LS, and different TD. By investigating these parameters, the research aims to gain insights into the thermal dynamics experienced by female students at the UAEU campus which can further lead to evaluate the thermal comfort (TC). Design/methodology/approach: The study involved three college-aged female participants simulating a daily walk to class on a arid tropical university campus. Real-time thermal measures were captured at 10-min intervals using the FLIR Thermal Camera. Statistical analyses, including the Kruskal–Wallis test and Don Bonferroni pairwise comparisons, were employed to assess significant differences in thermal conditions based on AC, TD, and LS. These tests rigorously analyzed the data to identify statistically significant variations in thermal conditions among the specified factors. Additionally, temperature data from the ECMWF ERA5 dataset, covering global climate from January 1940 to the present, was utilized. The GRADS application on a Linux-based system was used for data visualization and map chart creation. Findings: The study reveals a slight influence of AC on both Abaya temperatures (AT) and Skin temperatures (ST) during the summer, with more pronounced diurnal changes in the afternoon significantly affecting both metrics. Site characteristics minimally impact AT, but they have a noteworthy effect on ST. In the spring season, AT and ST remain unaffected by AC, while temporal fluctuations, particularly in the afternoon, exert a significant influence. LS variations show statistically insignificant impacts on both AT and ST. Additionally, temperature maps for specific 2023 dates provide distinctive trends in the UAE, offering insights into dynamic climatic conditions varying by season and TD. Originality/value: The study aims to address a knowledge gap by investigating the influence of AC, TD, and LS variations on physiological temperature change, specifically focusing on skin and AT. This investigation is conducted in the context of individuals exposed to thermal environmental factors within a arid tropical university campus setting. Practical implications: This research contributes to the existing knowledge by providing specific data on thermal conditions within the campus, enabling informed decision-making for creating a more comfortable and conducive environment for the students, thereby contributing to the goal of a sustainable campus.