scholarly journals A FUZZY LOGIC APPROACH FOR DRONE CAPABILITY ANALYSIS ON DISASTER RISK ASSESSMENT

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 485-489
Author(s):  
P. Zlateva ◽  
S. Hristozov ◽  
D. Velev
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Disaster Risk ◽  
External Input ◽  
Logic Model ◽  
Disaster Risk Assessment ◽  
Fuzzy Logic Model ◽  
Capability Analysis ◽  
Fuzzy Logic Approach ◽  
Logic Approach

<p><strong>Abstract.</strong> The paper proposes a fuzzy logic approach for drone capability analysis on disaster risk assessment. In particular, a fuzzy logic model is designed as a hierarchical system with several inputs and one output. The system inputs corresponds to the linguistic variables, describing the of levels of the external and internal input factors, which determine the capability levels of analysed drone in respect to disaster risk assessment. As external input factors are used, for example: disaster type (flood, landslide, wildfire); weather conditions (wind speed, fog, cloud cover); operational area (urban, mountain, plain), etc. As internal input factors are considered the drone characteristics such as drone type, flight performance (stall speed, turn radius, flight endurance), payload capabilities (camera resolution, accuracy, weight, sensors), etc. The fuzzy logic system output gives the level of the drone capability on disaster risk assessment in defined conditions. The model is designed in <i>Matlab</i> computer environment using Fuzzy Logic Toolbox. Several computer simulations are carried out to validate the proposed model. The designed fuzzy logic model is part of an information system for disaster risk management using drones, which is under development.</p>

scholarly journals Modeling sprinkler irrigation infiltration based on a fuzzy-logic approach

2017 ◽  
Vol 15 (1) ◽  
pp. e1201 ◽  
Author(s):  
Mohamed Abdel-Aziz Mattar ◽  
Mohamed S. El-Marazky ◽  
Khaled A. Ahmed
Keyword(s):  
Fuzzy Logic ◽  
Soil Surface ◽  
Logic Model ◽  
Water Infiltration ◽  
Coefficient Of Determination ◽  
Published Data ◽  
Fuzzy Logic Model ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Input Variables

In this study, the irrigation water infiltration rate (IR) is defined by input variables in linguistic terms using a fuzzy-logic approach. A fuzzy-logic model was developed using data collected from published data. The model was trained with three fuzzy membership functions: triangular (‘trimf’), trapezoid (trapmf), and pi (‘pimf’). The fuzzy system considered the number of irrigation events, applied water depth, polyacrylamide application rate, water application time, water electrical conductivity, soil surface slope, and soil texture components as input variables. The inputs were classified in terms of low, medium, and high levels. The output variable (i.e., IR) was rated in terms of five levels: very low, low, medium, high, and very high. Using statistical analysis, the values of IR resulting from the developed fuzzy-logic model were compared with the observations from the experiments. The results confirm that the agreement between the observations and predictive results was acceptable, except for fuzzy 'trimf'. The coefficient of determination provided the greatest value when using the 'trapmf' and 'pimf', with the value estimated for the 'pimf' slightly higher than that of 'trapmf'. Based on the results that were obtained, irrigation managers can use the fuzzy-logic approach to modify their field practices during the growing season to improve on-farm water management.

Risk assessment of cost overrun using fuzzy logic model

2022 ◽  
Author(s):  
G.B.S. Alekhya ◽  
K. Shashikanth ◽  
M. Anjaneya Prasad
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Logic Model ◽  
Cost Overrun ◽  
Fuzzy Logic Model

scholarly journals Supply chain risk assessment approach for process quality risks

2017 ◽  
Vol 34 (7) ◽  
pp. 940-954 ◽  
Author(s):  
Abhijeet Ghadge ◽  
Xie Fang ◽  
Samir Dani ◽  
Jiju Antony
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Supply Chain ◽  
Failure Modes ◽  
Design Stage ◽  
Security Risks ◽  
Content Type ◽  
Fuzzy Logic Approach ◽  
Logic Approach

Purpose The purpose of this paper is to proactively analyse and mitigate the root causes of the product and security risks. The case study approach examines the effectiveness of the fuzzy logic approach for assessing the product and process-related failure modes within global supply chain context. Design/methodology/approach The case study of a Printed Circuit Board Company in China is used as a platform for conducting the research. Using data triangulation, the data are collected and analyzed through interviews, questionnaires, expert opinions and quantitative modelling for some interesting insights. Findings Fuzzy logic approach for failure mode and effect analysis (FMEA) provides a structured approach for understanding complex behaviour of failure modes and their associated risks for products and processes. Today’s managers should conduct robust risk assessment during the design stage to avoid product safety and security risks such as recalls. Research limitations/implications The research is based on the single case study and multiple cases from different industry sectors may provide some additional insights. Originality/value The study attempts to mitigate the root causes of product and processes using fuzzy approach to FMEA in supply chain network.

Fuzzy Logic Approach in Risk Assessment

2018 ◽  
pp. 6789-6805
Author(s):  
Çetin Karahan ◽  
Esra Ayça Güzeldereli ◽  
Aslıhan Tüfekci
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Crucial Role ◽  
Quantitative Methods ◽  
Business Management ◽  
Human Reasoning ◽  
Action Plans ◽  
Fuzzy Logic Approach ◽  
Logic Approach

Risk is the likelihood of occurrence of any event that may obstruct the ability of organizations to achieve their strategic, financial and operational goals. It is of profound importance for the business management to detect risks and determine appropriate actions against in time. Risk assessment is a continuous and recursive process aimed at maximization of the use of opportunities while minimizing threats. There is a tendency in the field of risk assessment to prefer more quantitative methods to reduce unclarity. One such method is fuzzy logic. This study investigates fuzzy logic as an alternative to the classical methods that have been used for the purposes of risk assessment, which plays a crucial role in business action plans. Due to its similarity to the process of human reasoning and its success in cases of unclarity, fuzzy logic offers a number of advantages in this regard.

scholarly journals Identification of Safe Assembly Points in Emergencies in a Gas Refinery of the South Pars Gas Complex Using Fuzzy Logic Model

2019 ◽  
pp. 275-286
Keyword(s):  
Risk Assessment ◽  
Fuzzy Logic ◽  
Crisis Management ◽  
Oil And Gas ◽  
Treatment Plant ◽  
Logic Model ◽  
Fuzzy Logic Model ◽  
Accuracy And Precision ◽  
Focus Group Meeting ◽  
Gis Environment

INTRODUCTION: Crisis management is of critical importance in the oil and gas industries due to the increasing occurrence of accidents in these areas. One of the most important issues regarding crisis management in such industries is the identification of safety assembly points where employees should gather in emergencies. This study aimed to identify the safe points in a refinery using geographic information system (GIS) and fuzzy logic for emergency assembly. METHODS: Regarding the aim of the study purpose, the required data were collected, and a focus group meeting was held with experts to determine the criteria influencing the safety point zoning as well as high-risk units using the HAZOP method. After the identification of the criteria and sub-criteria affecting the zoning, the weight of each zoning parameter was calculated, and the safety zones were determined using the fuzzy logic model and its operators in the GIS environment. FINDINGS: According to the results of the risk assessment, the criteria and sub-criteria affecting zoning were divided into three categories of inconsistent (layer weight: 0.740), consistent (layer weight: 0.094), and access to exit routes (layer weight: 0.167). Moreover, the map results based on the fuzzy logic model revealed three safe points, including the vicinity of the fire station, clinic, and wastewater treatment plant in this refinery where the employees should gather in the event of emergencies. CONCLUSION: The results of this study showed that the selection of appropriate criteria in safe point zoning is of great importance in the emergencies in the industries. Moreover, an initial risk assessment can be effective in determining these criteria and sub-criteria. In addition, the fuzzy logic model has high accuracy and precision in determining the appropriate safe places.

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