In recent decades, the low-lying Flanders region (Belgium) has fallen victim to numerous flood events, causing substantial damage to buildings and infrastructure. In response to this, the Flemish government proposed a new approach that considers the level of risk as method of safety measurement. Using geographical information systems, this evolution has led to a comprehensive risk methodology, and more recently to the development of a flood risk assessment tool called LATIS. By estimating the potential damage and the number of casualties during a flood event, LATIS offers the possibility of performing risk analysis in a fast and effective way. This paper presents a brief overview of the currently used methodology for flood risk management in Flanders and its implementation in the LATIS tool. The usefulness of this new tool is demonstrated by a sequence of risk calculations, performed in the framework of climate change impacts on flood risk in Flanders.
Metaheuristic techniques, which are based on ideas of Artificial Intelligence, are among the best methods for solving computationally the GPS surveying network problem. In this paper, the ant colony optimization metaheuristic, which is inspired by the behavior of real ant colonies, is developed to efficiently provide a general framework for optimizing GPS surveying networks. In this framework, a set of ants co-operate together using an indirect communication procedure to find good GPS observation schedules. A GPS surveying network can be defined as a set of stations, coordinated by a series of sessions formed by placing receivers on the stations. The problem is to search for the best order in which to observe these sessions to give the best schedule at minimum cost. Computational results obtained by applying the proposed technique on several networks, with known and unknown optimal schedules, prove the effectiveness of the proposed metaheuristic technique to solve the GPS surveying network problem.
The paper outlines the main features of an intelligent decision support system based on existing and planned tools for optimising water management and flood risk reduction. Up to now, flood risk is increasing and environmental degradation is continuing; this requires developing robotic algorithms that can provide a degree of functionality for spatial representation and flexibility suitable for creating real-time solutions that maximize the urban flood protection measures. Moreover, the volume of data collected is growing rapidly and sophisticated means to efficiently optimise the data are essential. There is a need to develop a shared information system for flood management which will promote model and systems integration, monitoring. and decision making in strategic planning and emergency situations. This advanced area of research is a promising direction for producing an effective time-efficient solution to flood risk reduction where other methods failed. Therefore, the objective of this paper is to bring together innovative methods in the field of artificial intelligence, geoinformation technology and spatial and environmental planning to achieve more effective water management and flood risk reduction in Flanders.
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