Integral Seismic Risk Assessment through Fuzzy Models

Abstract

The usage of indicators as constituent parts of composite indices is an extended practice in many fields of knowledge. Even if rigorous statistical analyses are implemented, many of the methodologies follow simple arithmetic assumptions to aggregate indicators to build an index. One of the consequences of such assumptions can be the concealment of the influence of some of the composite index’s components. We developed a fuzzy method that aggregates indicators using non-linear methods and, in this paper, compare it to a well-known example in the field of risk assessment, called Moncho’s equation, which combines physical and social components and uses a linear aggregation method to estimate a level of seismic risk. By comparing the spatial pattern of the risk level obtained from these two methodologies, we were able to evaluate to what extent a fuzzy approach allows a more realistic representation of how social vulnerability levels might shape the seismic risk panorama in an urban environment. We found that, in some cases, this approach can lead to risk level values that are up to 80% greater than those obtained using a linear aggregation method for the same areas.

Designing and Manufacturing of Automatic Robotic Lawn Mower

“…(1) Averaging fuzzy Zadeh [28] proposed that fuzzy is a tool to convert fuzzy concepts that cannot be clearly expressed into quantitative expression, especially in the fuzzy characteristics of human speech expression, which has a good effect. The fuzzy theory has been applied to the management of medication for chronic patients [29], quality assessment of UAV photography services [30], as a rice transplanter control system [31], for aviation accident prevention and management [32], and in urban environmental risk assessment [33], which has made brilliant achievements in practice and academic research.…”