Fuzzy logic associated to the flood risk in Nova Lima / MG

Abstract

Data provided by Remote Sensing (SR) integrated with Geographic Information Systems (GIS) can be used in identifying, reducing and restructuring urban planning and environmental issues. In the case of severe natural phenomena such as floods, human lives, material goods and critical infrastructure can be seriously involved. This work develops a fuzzy multicriteria analysis model of decision support associated with Hierarchical Process Analysis (AHP). Along the study remote sensing and geoprocessing data and methods were used for mapping flood risks
areas within Nova Lima municipality in the estate of Minas Gerais, Brazil. Several input variables such as Digital Elevation Model (DEM), Slope, Soil Use and Cover, Preservation Area, Climate, Geology and Soil Type were used for building the models. A fuzzified
transformation by weight of evidence was performed to identify regions susceptible and not susceptible to flooding for each variable, resulting individual maps with values ranging from 0 to 1. Following the progress process the AHP method was used to multiply fuzzified maps using grades from research experts and bibliographic research producing a critical map with flood susceptibility levels. The study results were validated by comparing them with previous reference data obtained from the State Secretariat of Environment and Sustainable Development (SEMAD), the Mineral Resources Research Company (CPRM) and local information. Applying developed methodology the validation process presented an 86% degree of consistency. The straightforward running of process, the low cost and the results consistency highlighted the innovative content of the methodology. Therefore, identification of susceptible to flood risk areas based on the fuzzy and AHP methodology has proved to be valuable in identifying sensitive areas requiring special government attention and investments for protection, prevention and mitigation actions. Thus, the study is a contribution to disaster and risk management in flood areas.