No more flying blind: Leveraging weather forecasting for clear-cut risk-based decisions

• An expert system for drone’s missions planning. • A four-phase methodology to assess weather-related feasibility indexes over time. • A case study related to an aero-photogrammetry mission. • Machine learning algorithms to enhance weather forecasting processes. Unmanned aircraft systems (UASs) have experienced a notable surge in applications, particularly with the increasing deployment of vertical take-off and landing (VTOL) vehicles in urban environments, which are more flexible in comparison to traditional aircraft. Nevertheless, the advantages of using VTOLs come with an increase in operational risks, too. Although there are approaches to support the fulfillment of safety objectives for VTOL operations, none of them specifically consider the type of weather information needed to guide decision-making successfully. Having detailed weather forecasts within operational areas can help avoid unwanted outcomes while assuring safe operations and mission success. On this basis, this paper proposes an innovative methodology to support decision-making in VTOLs missions, emphasizing the importance of weather forecasting practices. The decision support methodology presented in this study involves four phases, which consider different timespans (i.e., from more than two weeks before up to two hours before the mission), eventually assessing dedicated feasibility indexes. A case study is proposed to show how the methodology could be implemented into a decision support system with the objective of guiding VTOL decision makers in identifying the most suitable vehicle to ensure successful operations in various contexts from innovative air mobility solutions towards industrial inspection practices.