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Since natural disasters may cause big human and material losses, the Brazilian government are specially interested in ways to avoid it. Among these ways, the use of computational technologies are specially interesting for monitoring and warning population about such disasters. At this work we present an approach, developed at the Brazilian National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN), for watershed areas delimitation and characterization, based on digital images and geographical databases, using only open source tools. The proposed approach achieves consistent and useful results on operation environment, providing a significant helping for disasters management. Finally, we present some examples of delimited and characterized watersheds, in different regions of Brazil, including one in the Amazon rainforest area. The presented approach is already operational at the situation room in CEMADEN.

This paper describes a study performed in the Brazilian governmental center, named Cemaden, responsible to monitor natural disasters and sending alerts to the vulnerable community involved. The target of our study is the complex architecture of Cemaden’s Observational Network. It comprises constituent systems (CS) provided by other partners organisms, which operation and maintenance are out of Cemaden con trolling. The correct functioning of those heterogeneous CSs represents a major challenge in this type of system. Thus the comprehension of dependability issues regarding the information flow in the integration of the CSs is essential. Our study compares the current integration architecture based on syntax rules with the use of an ontology-based integration. The advantages of the proposed architecture are discussed in a case study.

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In this article a set of well-known computational intelligence techniques such as Decision Trees, Fuzzy Logic, and Multi-objective Optimization Genetic Algorithm are combined to generate a novel hybrid method which is called MoGFT-I: Multi-objective Genetic Fuzzy Rule Based System supported by a Decision Tree with Improved Interpretability. The output of the proposed supervised learning method is a set of Mamdani-type fuzzy systems which are optimized and distributed along a Pareto curve by considering two conflicting attributes: accuracy and interpretability. The MoGFT-I method is then applied to the Cart and Pole control problem such that a set of feedback controller are designed to control this unstable nonlinear dynamical system.

In the last years, natural disasters have become more frequent and their impacts have reached a large number of people. When these events happen in unprepared regions, they can cause human and material losses. The consequences can be even more catastrophic when the responsible entities are not properly prepared. To minimize the impacts of extreme weather events, especially extreme rain which can trigger severe floods, this paper proposes a methodological approach to monitor floods through open source software, based on watershed's delineation. The suggested approach can be a tool to support disaster risk reduction planning.