Pairwise Elicitation for a Decision Support Framework to Develop a Flood Risk Response Plan

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
MiguelAndres Guerra ◽  
Yekenalem Abebe
Keyword(s):  
Decision Making ◽  
Flood Risk ◽  
Flood Hazard ◽  
Addis Ababa ◽  
Pairwise Comparison ◽  
Multicriteria Decision Making ◽  
Risk Response ◽  
Hazard And Risk ◽  
Response Plan ◽  
Decision Making Framework

There are several ways of quantifying flood hazard. When the scale of the analysis is large, flood hazard simulation for an entire city becomes costly and complicated. The first part of this paper proposes utilizing experience and knowledge of local experts about flood characteristics in the area in order to come up with a first-level flood hazard and risk zoning maps, by implementing overlay operations in Arc GIS. In this step, the authors use the concept of pairwise comparison to eliminate the need for carrying out a complicated simulation to quantify flood hazard and risk. The process begins with identifying the main factors that contribute to flooding in a particular area. Pairwise comparison was used to elicit knowledge from local experts and assigned weights for each factor to reflect their relative importance toward flood hazard and risk. In the second part of this paper, the authors present a decision-making framework to support a flood risk response plan. Once the highest risk zones have been identified, a city can develop a risk response plan, for which this paper presents a decision-making framework to select an effective set of alternatives. The framework integrates tools from multicriteria decision-making, charrette design process to guide the pairwise elicitation, and a cost-effective analysis to include the limited budget constraint for any city. The theoretical framework uses the city of Addis Ababa for the first part of the paper. For the second part, the paper utilizes a hypothetical case of Addis Ababa and a mock city infrastructure department to illustrate the implementation of the framework.

GIS based Development of MCDM Model for Flood Risk Management across Godavari Lower Sub-Basin of India

2020 ◽  
Author(s):  
B. Thanga Gurusamy ◽  
Avinash D Vasudeo ◽  
Aniruddha Dattatraya Ghare
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Evolutionary Algorithms ◽  
Objective Function ◽  
Flood Risk ◽  
Flood Hazard ◽  
Flood Risk Management ◽  
Flood Protection ◽  
Hazard Zonation ◽  
Hazard Risk

<p><strong>Abstract: </strong>Because of the uncertainty and high cost involved, the Absolute Flood Protection has not been considered as a rational decision. Hence the trend is to replace Absolute Flood Protection strategy by Flood Risk Management Strategy. This Paper focus on the development of Multiple Criteria Decision Making (MCDM) model towards Flood Risk Management (FRM) across Godavari Lower Sub-Basin of India using GIS based methodologies for Flood Hazard Zonation in order to achieve global minimum of the Flood predicted Risk level.  Flood Hazard Zone Map for the historical flood events obtained with the use of GIS based Digital Elevation Models across the study area have been presented and used for the estimation of Hazard Risk. Uncertainty (or Control) Risk levels of each Flood estimated using various Flood Forecasting methodologies have been compared for the selected locations of the study area. Effectiveness of Passive Flood Protection Measures in the form of Flood Levees has been quantitatively analyzed for the increase in the Opportunity Risk and corresponding reduction in the Flood Hazard Risk. Various types of Multi-Objective Evolutionary Algorithms (MOEAs) have been used  to determine a Compromise solution with conflicting criteria between Hazard Risk and Opportunity (or Investment) Risk and the results were compared for each of the selected levels of Flood estimated with corresponding uncertainty. Traditional optimization method in the form of Pareto-Optimal Front have also been graphically depicted for the minimization of both Hazard Risk Objective function and Opportunity Risk Objective Function and compared with those obtained using MOEAs. Watershed wise distribution of optimized Flood Risk variation across the Sub-basin has been presented graphically for both the cases of with and without active Flood Routing Measures. <strong>Keywords:  </strong>Flood Risk Management; GIS based Flood Hazard Zonation; Multi-Criteria Decision Making; Multi-Objective Evolutionary Algorithms; Godavari Lower Sub-Basin of India;</p>

On Some Advancements within Certain Multicriteria Decision Making Support Methodology

2013 ◽  
Vol 2 (2) ◽  
pp. 143 ◽  
Author(s):  
Pawel Tadeusz Kazibudzki ◽  
Andrzej Z Grzybowski
Keyword(s):  
Decision Making ◽  
Pairwise Comparison ◽  
Multicriteria Decision Making ◽  
Scientific Paper ◽  
Analytic Hierarchy ◽  
Management Activity ◽  
Multicriteria Decision ◽  
Managerial Decisions ◽  
The Right ◽  
Decision Making Support

Deriving true priority vectors from intuitive pairwise comparison matrices (PCMs) and consistency measurement of decision makers judgments about their genuine weights are crucial issues within the multicriteria decision making support methodology called Analytic Hierarchy Process (AHP). The most popular procedure in the ranking process, constitutes the Right Eigenvector Method (REV). The inventor of the AHP convinces that as long as inconsistent PCMs are allowed in the AHP none of the other existing procedures qualify and the REV provides the only right solution in this process. The objective of this scientific paper is to examine if the former opinion can be considered as experimentally confirmed. For this purpose it was decided to apply Monte Carlo methodology. However, rather than simulate and analyze simulations results for a single PCM, as it has been done so far by many other authors, we decided to design and analyze computer simulations results for a singular model of the AHP framework. Our findings lead to inevitable conclusion that the REV cannot longer be perceived as a dominant procedure within the AHP methodology, especially when nonreciprocal PCMs are considered. It was verified empirically in our research that in the situation when nonreciprocal PCMs are considered the REV impoverishes the entire AHP methodology by its lack of PCMs inconsistency measure in such cases. Moreover, it provides less accurate rankings for a particular decision in comparison to other presented methods. It was also unequivocally verified that the enforced reciprocity of PCM leads directly to worse estimates of priorities weights. Altogether, it seems very important from the perspective of methodology supporting multicriteria decision making, the crucial process embedded in most of management activity. In the consequence, because the REV recedes other prioritization procedures available for the AHP methodology, it is advised to consider them instead, especially under some circumstances of an important and very tight managerial decisions.

scholarly journals Index-based flood risk assessment for Metro Manila

2020 ◽  
Vol 20 (3) ◽  
pp. 851-859 ◽  
Author(s):  
C. J. Rubio ◽  
I. S. Yu ◽  
H. Y. Kim ◽  
S. M. Jeong
Keyword(s):  
Risk Assessment ◽  
Land Use Planning ◽  
Flood Risk ◽  
Flood Hazard ◽  
The Philippines ◽  
Flood Risk Assessment ◽  
Disaster Insurance ◽  
Metro Manila ◽  
Hazard And Risk ◽  
Populated Region

Abstract This study focuses on index-based flood risk assessment in Metro Manila, the capital region of the Philippines and most densely populated region in the country. Its objective is to properly address urban characteristics in flood risk assessment by introducing a specific urban-type set of physical, social, economic and ecological indicators. Analytical hierarchy process (AHP) was used to quantify the optimal selection weights for each of the selected 14 indicators. Five levels of flood risk will be presented in spatial maps using geographic information system (GIS) ranging from Very Low Risk to Very High Risk. Results of this study are expected to aid in understanding flood hazard and risk in Metro Manila. Moreover, the resulting flood risk information can be used as a decision tool in policy making, land-use planning, developing guidelines and countermeasures and flood disaster insurance.

scholarly journals Matrix of pairwise comparison: decision making with the analytic network process

2019 ◽  
Vol 110 ◽  
pp. 02042
Author(s):  
Aliya Akhmadullina ◽  
Svetlana Vasilyeva ◽  
Tatyana Yakovleva ◽  
Svetlana Vopiyashina ◽  
Raisa Kraineva
Keyword(s):  
Decision Making ◽  
Environmental Planning ◽  
Pairwise Comparison ◽  
Multicriteria Decision Making ◽  
Analytic Network Process ◽  
Analytic Hierarchy ◽  
Pairwise Comparison Matrix ◽  
Network Process ◽  
Qualitative Factors ◽  
The Right

This article describes a method for analyzing hierarchies; identifies the problems with inconsistent judgments. The proof is given that the most effective tool allowing one to make the right decisions with inconsistencies is the introduction of the eigenvector on environmental planning and management. The Analytic Hierarchy Process (AHP) is a method for decision making, which includes qualitative factors. In this method, ratio scales are obtained from ordinal scales, which are derived from individual judgments for qualitative factors using the pairwise comparison matrix. This paper describes the applicability of a multicriteria decision-making method, specifically, the analytic network process.

scholarly journals Comparing best-worst method and full consistency method in a fuzzy environment

2022 ◽  
Vol 11 (2) ◽  
pp. 181-192
Author(s):  
Arash Haqbin
Keyword(s):  
Decision Making ◽  
Real World ◽  
Pairwise Comparison ◽  
Multicriteria Decision Making ◽  
Fuzzy Environment ◽  
Study Results ◽  
Mcdm Method ◽  
Consistency Method ◽  
Best Worst Method

Multicriteria Decision Making (MCDM) is one the most important branches of decision theory. Due to the fact that MCDM methods have the utmost significance in management, scholars try to develop more MCDM methods. Since calculating the weights of criteria is an important step in any MCDM method, increasing the accuracy of weight calculating methods can highly affect these methods. This accuracy can be improved by less pairwise comparison between criteria. To this end, the present study seeks to make a comparison between two new weight calculating techniques, namely BWM and FUCOM in a fuzzy environment using a real-world case study Results of this study shows that FUCOM-F provides more reliable results compared to FBWM since its consistency is less than FBWM by a great amount.

scholarly journals Urban Flood Risk Assessment: Integration of Decision Making and Machine Learning

2022 ◽  
Author(s):  
Fereshteh Taromideh ◽  
Ramin Fazloula ◽  
Bahram Choubin ◽  
Alireza Emadi ◽  
Ronny Berndtsson
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Flood Risk ◽  
Flood Hazard ◽  
Hazard Maps ◽  
Risk Map ◽  
Flood Vulnerability ◽  
Urban Flood ◽  
Machine Learning Methods ◽  
Urban Flood Risk

Urban flood risk mapping is an important tool for the mitigation of flooding in view of human activities and climate change. Many developing countries, however, lack sufficiently detailed data to produce reliable risk maps with existing methods. Thus, improved methods are needed that can improve urban flood risk management in regions with scarce hydrological data. Given this, we estimated the flood risk map for Rasht City (Iran), applying a composition of decision-making and machine learning methods. Flood hazard maps were produced applying six state-of-the-art machine learning algorithms such as: classification and regression trees (CART), random forest (RF), boosted regression trees (BRT), multivariate adaptive regression splines (MARS), multivariate discriminant analysis (MDA), and support vector machine (SVM). Flood conditioning parameters applied in modeling were elevation, slope angle, aspect, rainfall, distance to river (DTR), distance to streets (DTS), soil hydrological group (SHG), curve number (CN), distance to urban drainage (DTUD), urban drainage density (UDD), and land use. In total, 93 flood location points were collected from the regional water company of Gilan province combined with field surveys. We used the Analytic Hierarchy Process (AHP) decision-making tool for creating an urban flood vulnerability map, which is according to population density (PD), dwelling quality (DQ), household income (HI), distance to cultural heritage (DTCH), distance to medical centers and hospitals (DTMCH), and land use. Then, the urban flood risk map was derived according to flood vulnerability and flood hazard maps. Evaluation of models was performed using receiver-operator characteristic curve (ROC), accuracy, probability of detection (POD), false alarm ratio (FAR), and precision. The results indicated that the CART model is most accurate model (AUC = 0.947, accuracy = 0.892, POD = 0.867, FAR = 0.071, and precision = 0.929). The results also demonstrated that DTR, UDD, and DTUD played important roles in flood hazard modeling; whereas, the population density was the most significant parameter in vulnerability mapping. These findings indicated that machine learning methods can improve urban flood risk management significantly in regions with limited hydrological data.

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