Design and Implementation of a Fuzzy Inference Model for Mapping the Sustainability of Energy Crops

2017 ◽  
pp. 657-678
Author(s):  
Fausto Cavallaro ◽  
Luigi Ciraolo
Keyword(s):  
Environmental Sustainability ◽  
Input Data ◽  
Fuzzy Inference ◽  
Renewable Energy Sources ◽  
Energy Crops ◽  
Environmental Evaluation ◽  
Uncertain Input ◽  
Inference Systems ◽  
Uncertain Input Data ◽  
Biomass For Energy

Energy crops are positioned as the most promising renewable energy sources. Over recent years, the use of biomass has been growing significantly, especially in countries that have made a strong commitment to renewable sources in their energy policies. One of the aspects of the use of biomass for energy is that it is still controversial with regard to full environmental sustainability. Unfortunately, the existing environmental evaluation tools in many cases are unable to manage uncertain input data. Fuzzy-set-based methods, instead, have proved to be able to deal with uncertainty in environmental topics. The idea of this chapter is to reproduce a solution by decoding it from the domain of knowledge with the calculus of fuzzy “if-then” rules. A methodology based on Fuzzy Inference Systems (FIS) is proposed to assess the environmental sustainability of biomass.

Design and Implementation of a Fuzzy Inference Model for Mapping the Sustainability of Energy Crops

2015 ◽  
pp. 47-68
Author(s):  
Fausto Cavallaro ◽  
Luigi Ciraolo
Keyword(s):  
Environmental Sustainability ◽  
Input Data ◽  
Fuzzy Inference ◽  
Renewable Energy Sources ◽  
Energy Crops ◽  
Environmental Evaluation ◽  
Uncertain Input ◽  
Inference Systems ◽  
Uncertain Input Data ◽  
Biomass For Energy

Energy crops are positioned as the most promising renewable energy sources. Over recent years, the use of biomass has been growing significantly, especially in countries that have made a strong commitment to renewable sources in their energy policies. One of the aspects of the use of biomass for energy is that it is still controversial with regard to full environmental sustainability. Unfortunately, the existing environmental evaluation tools in many cases are unable to manage uncertain input data. Fuzzy-set-based methods, instead, have proved to be able to deal with uncertainty in environmental topics. The idea of this chapter is to reproduce a solution by decoding it from the domain of knowledge with the calculus of fuzzy “if-then” rules. A methodology based on Fuzzy Inference Systems (FIS) is proposed to assess the environmental sustainability of biomass.

A Fuzzy Approach to Disaster Modeling

2013 ◽  
pp. 1038-1055
Author(s):  
Jan Stoklasa
Keyword(s):  
Disaster Response ◽  
Initial Phase ◽  
Input Data ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Linguistic Variables ◽  
Rescue Services ◽  
Uncertain Input ◽  
Uncertain Input Data ◽  
Rule Bases

The decision making process of the Emergency Medical Rescue Services (EMRS) operations centre during disasters involves a significant amount of uncertainty. Decisions need to be made quickly, and no mistakes are tolerable, particularly in the case of disasters resulting in a large number of injured people. A multiphase linguistic fuzzy model is introduced to assist the operator during the initial phase of the medical disaster response. Based on uncertain input data, estimating the severity of the disaster, the number of injured people, and the amount of forces and resources needed to successfully deal with the situation is possible. The need for reinforcements is also considered. Fuzzy numbers, linguistic variables and fuzzy rule bases are applied to deal with the uncertainty. Outputs provided by the model (severity of the disaster, number of reinforcements needed etc.) are available both as fuzzy sets (for the purposes of disaster planning) and linguistic terms (for emergency call evaluation purposes).

A Fuzzy Approach to Disaster Modeling

2012 ◽  
pp. 564-582
Author(s):  
Jan Stoklasa
Keyword(s):  
Disaster Response ◽  
Initial Phase ◽  
Input Data ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Linguistic Variables ◽  
Rescue Services ◽  
Uncertain Input ◽  
Uncertain Input Data ◽  
Rule Bases

The decision making process of the Emergency Medical Rescue Services (EMRS) operations centre during disasters involves a significant amount of uncertainty. Decisions need to be made quickly, and no mistakes are tolerable, particularly in the case of disasters resulting in a large number of injured people. A multiphase linguistic fuzzy model is introduced to assist the operator during the initial phase of the medical disaster response. Based on uncertain input data, estimating the severity of the disaster, the number of injured people, and the amount of forces and resources needed to successfully deal with the situation is possible. The need for reinforcements is also considered. Fuzzy numbers, linguistic variables and fuzzy rule bases are applied to deal with the uncertainty. Outputs provided by the model (severity of the disaster, number of reinforcements needed etc.) are available both as fuzzy sets (for the purposes of disaster planning) and linguistic terms (for emergency call evaluation purposes).

On Topological Derivatives for Elastic Solids with Uncertain Input Data

2009 ◽  
Vol 141 (3) ◽  
pp. 569-595 ◽  
Author(s):  
I. Hlaváček ◽  
A. A. Novotny ◽  
J. Sokołowski ◽  
A. Żochowski
Keyword(s):  
Input Data ◽  
Elastic Solids ◽  
Topological Derivatives ◽  
Uncertain Input ◽  
Uncertain Input Data
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