Investigating Indian oil sardine aggregation events in coastal waters of the southeastern Arabian sea.

The southwest coast of India experiences frequent Indian oil sardine (IOS) nearshore aggregation events, especially in the coastal waters off Kerala. These ephemeral dense IOS aggregation events are known as "Sardine Run". To investigate the reason and provide a scientific basis for these sporadic events, satellite/model-derived physical, meteorological, and biological parameters were analysed. Sea Surface Temperature during a majority of events was in the range of 26-29 °C, agreeing with the reported temperature conditions for IOS in the Arabian Sea. Additionally, a marginal lowering of SST as an effect of precipitation before most of the events might have attracted IOS towards the near-coastal waters in addition to the phytoplankton diet availability, resulting in the aggregation event. However, different scenarios also depicted coastal warming and probable hypoxic conditions in degrading IOS habitat and resulting in beach aggregation events. During most of the IOS aggregation events, the wind and surface current direction was alongshore/coastward, which complemented the propagation of live IOS shoals towards the beach.

Utilizing geospatial intelligence and user modeling to allow for a customized health awareness campaign during the pandemic: The case of COVID-19 in Saudi Arabia.

BACKGROUND: As of 2022, people are getting better at learning how to coexist with the Covid-19 global pandemic. In Saudi Arabia, many attempts have been made to raise public health awareness. However, most health awareness campaigns are generic and might not influence the desired behavior among individuals. OBJECTIVES: This study aims to apply geospatial intelligence and user modeling to profile the districts of the city of Jeddah. This customized map can provide a baseline for a customized health awareness campaign that targets the locals of each district individually based on the virus spread level. METHODOLOGY: It is ongoing research, which has resulted in the creation of a health messages library in the first phase [1]. This paper focuses on a second phase of the research study, which aims to provide a customized baseline for this campaign by applying the geospatial artificial intelligence technique known as space-time cube (STC). STC was applied to create a local map of the Saudi city of Jeddah, representing three different profiles for the city's districts. The model is built using valid COVID-19 clinical data obtained from one of Jeddah's general hospitals. RESULTS AND IMPLICATIONS: When applied, STC displays three profiles for the districts of Jeddah city: high infection, moderate infection, and low infection. To assess the geo-intelligent map, a new instrument was created and validated. The usability and practicality of this map were quantitatively evaluated in a cross-sectional survey using the goal-question-metric measurement framework, and a total of 43 participants filled out the questionnaire. The results indicate that the geo-intelligent map is suitable for everyday use, as evidenced by the participants' responses. We argue that the developed instrument can also be used to assess any geo-intelligence map. This research provides a legitimate approach to customizing health awareness messages during pandemics.