Positioning is an essential element in most Internet of Things (IoT) applications. Global Positioning System (GPS) chips have high cost and power consumption, making it unsuitable for long-range (LoRa) and low-power IoT devices. Alternatively, low-power wide-area (LPWA) signals can be used for simultaneous positioning and communication. We summarize previous studies related to LoRa signal-based positioning systems, including those addressing proximity, a path loss model, time difference of arrival (TDoA), and fingerprint positioning methods. We propose a LoRa signal-based positioning method that uses a fingerprint algorithm instead of a received signal strength indicator (RSSI) proximity or TDoA method. The main objective of this study was to evaluate the accuracy and usability of the fingerprint algorithm for large areas in the real world. We estimated the locations using probabilistic means based on three different algorithms that use interpolated fingerprint RSSI maps. The average accuracy of the three proposed algorithms in our experiments was 28.8 m. Our method also reduced the battery consumption significantly compared with that of existing GPS-based positioning methods.

Digital twin technology based on building a virtual digital city similar to a real one enables the simulation of urban phenomena or the design of a city. A geospatial platform is an essential supporting component of digital twin cities. In this study, we propose a planetary-scale geospatial open platform that can be used easily in the most widely used game engine environment. The proposed platform can visualize large-capacity geospatial data in real time because it organizes and manages various types of data based on quadtree tiles. The proposed rendering tile decision method provides constant geospatial data visualization according to the camera controls of the user. The platform implemented is based on Unity3D, and therefore, one can use it easily by importing the proposed asset library. The proposed geospatial platform is available on the Asset Store. We believe that the proposed platform can meet the needs of various three-dimensional (3-D) geospatial applications.

Urban pluvial flooding driven by localized extreme rainfall increasingly exceeds the capacity of metropolitan drainage systems. Manhole surcharge overflow interacting with surface runoff produces complex inundation dynamics that are difficult to capture in real time. High-fidelity 1D–2D coupled numerical models are too computationally expensive for operational deployment, whereas purely data-driven deep-learning surrogates, although fast, do not enforce conservation laws or provide physically interpretable behaviour. We propose a modular coupled physics-informed neural network (MC-PINN) that bridges this gap. MC-PINN consists of a 1D PINN for sewer network flow governed by the Saint-Venant equations and a 2D PINN for surface flow governed by shallow-water equations, coupled through manhole overflow acting as a boundary condition. Mass continuity is enforced at the interface and momentum is strongly constrained via physics-based residual losses, enabling a hybrid surrogate with near real-time inference potential. To demonstrate feasibility, we present a manufactured-solution proof-of-concept in which a 1D advection PINN and a 2D diffusion PINN are coupled via a shared interface signal. The example shows that MC-PINN can jointly approximate both PDE fields and a consistent interface, supporting the mathematical viability of the modular coupling.

Climate change has increased the intensity and frequency of hazard events, resulting in disaster risks that exceed historical experience. In highly urbanized countries such as South Korea, heavy and extreme rainfall has become a major climate-related hazard, leading to concentrated human and economic losses. While extreme rainfall can be partially anticipated through meteorological radar observations, official forecasts, and pattern-based prediction models, such hazard-focused approaches are insufficient to fully assess disaster risk in urban areas. This is because actual impacts are strongly influenced by exposure, vulnerability, and cascading effects, which may evolve into complex disasters.In line with the IPCC and UNDRR disaster risk framework, this study emphasizes the need to anticipate secondary hazards and cascading risk events that may develop into complex disasters under extreme rainfall conditions. To address this challenge, a scenario generation method for extreme rainfall–induced complex disasters is proposed. The method integrates three key components: (1) regional exposure and vulnerability characteristics, including population distribution, industrial activities, transportation networks, and critical infrastructure; (2) secondary hazard and impact information derived from historical disaster records; and (3) interrelationships and correlations among different hazard and disaster types.Using a weighted analytical framework, the proposed approach generates representative scenarios with high likelihood as well as extreme scenarios with lower likelihood but potentially high impacts. These scenarios support a risk-informed understanding of possible disaster pathways and provide actionable prior information for preparedness planning, emergency response, and scenario-based training. The results contribute to strengthening disaster risk reduction and enhancing urban resilience against climate-related extreme rainfall–induced complex disasters.This work was supported by Electronics and Telecommunications Research Institute(ETRI) grant funded by the Korean government [26ZR1300, Development of Technology for the Urban Extreme Rainfall Response Platform].