Determining the Most Sensitive Socioeconomic Parameters for Quantitative Risk Assessment
Abstract
:1. Introduction and Statement of Problem
1.1. Overview of the Research
1.2. Research Gap, Research Objectives, and Significance
2. Method
2.1. Study Area Selection
2.2. Hazards in the Study Area
2.3. Available Socioeconomic Indicators to Assess Risk in the Study Area
2.4. Non-Linear Programming to Determine the Most Sensitive Indicators
2.4.1. Unconstrained Non-Linear Programming
2.4.2. Constrained Non-Linear Programming
2.5. Development of Non-Linear Programming System
2.6. Solution of Non-Linear Programming System with Karush-Kuhn-Tucker (KKT) Conditions
- (1)
- f(y) is to be feasible to apply the above constraints (iv) and (v).
- (2)
- Gradients of (iii), (iv), and (v) improve objectives and satisfies the following equations:
- (3)
- It satisfies to the positive Lagrangian multiplier .
2.7. Statistical Analysis to Detect Significant Change
3. Results and Discussion
3.1. Selection of the Most Significant Indicators
3.2. Implication of the Most Sensitive Indicators
4. Conclusions and Recommendations
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Domain | Indicators | Impact on Risk | Data Source and Data Unit |
---|---|---|---|
Exposure | Cropped Area | Negative impact on risk due to its exposure to hazard [39,40]. | Data source: [41]. Data unit: Percentage of Cropped Area per unit of administrative area. |
Number of Household | Increased number of households causes increased risk [42,43]. | Data source: [29]. Data unit: Percentage of Number of Household per unit of administrative area. | |
Population Density | Increased population density increases exposed population to risk [43,44]. | Data source: [29]. Data unit: Total number of population per unit of administrative area. | |
Sensitivity | Female to Male Ratio | Female population are more sensitive to risk than male population. Increased number of female populations increases risk. | Data source: [29]. Data unit: Ratio of female to male population. |
Poverty Rate | Poor people are sensitive to hazards. So, higher poverty rates are indicative of higher risk due to same hazard. | Data source: [29]. Data unit: Percentage of extreme poor lies below poverty line. | |
Dependent Population | Dependent population in an area are the women, children, and elderly people. These group of population are considered as less able to adaptation against risk [42,44,45]. | Data source: [29]. Data unit: Percentage of summation of women, children and elderly population to the total population of an administrative unit. | |
Disabled People | Physically and mentally disabled people are more sensitive to hazard because of their inability and slow response during a hazard event [46]. | Data source: [29]. Data unit: Percentage of total disabled people to total number of population in an administrative unit. | |
Unemployed Population | Unemployment decreases the coping capacity and increases the sensitivity and susceptibility to risk [47]. | Data source: [29]. Data unit: Percentage of total unemployed population to total population in an administrative unit. | |
Adaptive Capacity | Growth center | Growth center is an economic indicator. Increased number of this indicator indicates better economic strength and better adaptive capacity against vulnerability [48]. | Data source: [29]. Data unit: Number of growth center per 5000 of population in an administrative unit. |
Plantation | Plantation is considered as a buffer against storm surge hazard that reduces the initial thrust of the hazard. Reduction of hazard means reduction of risk [49]. | Data source: [34]. Data unit: Forest area (natural and artificial) per unit of administrative area. | |
Aquaculture | Shrimp cultivation is the dominant aquaculture in the study area. Aquaculture is considered as an alternative livelihood to adapt against salinity hazard. | Data Source: [34]. Data unit: Shrimp cultivated area per unit of administrative area. | |
Cyclone shelter | Cyclone shelter is a structural adaptive measure against storm surge hazard. Increased number of cyclone shelter reduces number of human casualty and thus reduces storm surge risk [43,50]. | Data source: [51]. Data unit: Number of cyclone shelter per unit of administrative area. | |
Cropping intensity | Cropping intensity is an indicator of agricultural activity. Increased cropping intensity means increased adaptive capacity that reduces risk against hazard [39,40,52] | Data source: [41]. Data unit: Percentage of gross cropped area per net cropped area in an administrative unit. | |
GDP | Gross Domestic Product (GDP) is an economic indicator. Higher GDP means better ability to recover from loss and reduce risk from hazard [53]. | Data source: [29]. Data unit: Gross Domestic Product per capita. | |
Irrigation Equipment | Shallow tube-well (Stw), Deep tube-well (Dtw), and Low Lift Pump (LLP) are known irrigation equipment in the study area. Increased number of Irrigation Equipment enable a farmer to better adapt with the hazard and thus reduce risk. | Data source: [29]. Data unit: Number of irrigation equipment per unit of cropped land area. | |
Polder Area | Polder is an encircled embankment constructed to prevent flood in the study area. Increased number of polders reduces flood and thus reduces flood risk [43,54]. | Data source: [34]. Data unit: Percentage of total poldered (embanked) area per administrative unit. | |
Presence of Lifeline | Lifeline is represented by water supply, sanitation and electricity. Higher number of lifeline utilities are considered to increase adaptive capacity against vulnerability and thus reduces risk [43,44,45]. | Data source: [29]. Data unit: Percentage of tap water and other pond types surface water sources and percentage of connected sanitary and electricity lines per unit area of an administrative unit. | |
Loan | Loan is considered as the credit facility by co-operative society and banks, particularly to recover from loss due to hazard. Increased loan facilities thus reduce risk due to hazard [55]. | Data source: [29]. Data unit: Percentage of total account holder per total number of populations in an administrative unit. | |
Literacy Rate | Literate people know better how to adapt with the vulnerability and reduce risk [42,44,45] | Data source: [29]. Data unit: Percentage of number of literate people per unit of administrative area. | |
Number of Health care Provider | Health care providers play an important role to reduce human casualty during a hazard, which acts to reduce vulnerability and risk of the community [56]. | Data Source: [29]. Data unit: Percentage of health care provider compared to total population in an administrative unit. | |
Paka and Semi-paka house | Paka and Semi-paka houses represent households which are structurally strong to resist impacts of hazard. Presence of these housing types reduces risk [43,45]. | Data source: [29]. Data unit: Percentage of Paka and Semi-paka houses compared to total number of households in an administrative unit. | |
Communication Infrastructure | Communication infrastructure is represented by all types of structural measures related to communication. It acts as an adaptive capacity for a community and reduces vulnerability and risk during a hazard [43,44,57]. | Data source: [29]. Data unit: Weighted sum of length of different types of structural measures used for communication purpose in an administrative unit. | |
Road Density | Increased road density in an area increases the mobility during the time of hazard. This makes it possible to utilize other adaptive measures that reduces risk. | Data source: [29]. Data unit: Total road length in an administrative unit. |
Indicators | Coefficient of Objective Function | Lower Limit | Upper Limit | Range between Lower Limit and Upper Limit | Rank |
---|---|---|---|---|---|
Cropped Area | 0.15901201 | 1.59 × 10−1 | 1.59 × 10−1 | 6.47 × 10−8 | 1 |
Number of households | 0.42011697 | 4.20 × 10−1 | 4.20 × 10−1 | 7.27 × 10−8 | 2 |
Population density | 0.42087102 | 4.21 × 10−1 | 4.21 × 10−1 | 7.27 × 10−8 | 3 |
Cyclone shelter | 0.044980433 | 4.50 × 10−2 | 6.02 × 10−2 | 1.52 × 10−2 | 4 |
Plantation | 0.046954986 | 4.70 × 10−2 | 6.29 × 10−2 | 1.59 × 10−2 | 5 |
Polder Area | 0.049170973 | 4.92 × 10−2 | 6.58 × 10−2 | 1.66 × 10−2 | 6 |
Growth Centre | 0.052033969 | 5.20 × 10−2 | 6.97 × 10−2 | 1.76 × 10−2 | 7 |
GDP | 0.055189127 | 5.52 × 10−2 | 7.39 × 10−2 | 1.87 × 10−2 | 8 |
Irrigation Equipment | 0.057194489 | 5.72 × 10−2 | 7.66 × 10−2 | 1.94 × 10−2 | 9 |
Paka and Semi- paka house | 0.05755829 | 5.76 × 10−2 | 7.71 × 10−2 | 1.95 × 10−2 | 10 |
Loan | 0.059529224 | 5.95 × 10−2 | 7.97 × 10−2 | 2.02 × 10−2 | 11 |
Communication Infrastructure | 0.059854204 | 5.99 × 10−2 | 8.01 × 10−2 | 2.03 × 10−2 | 12 |
Cropping intensity | 0.062339437 | 6.23 × 10−2 | 8.35 × 10−2 | 2.11 × 10−2 | 13 |
Aquaculture | 0.062434786 | 6.24 × 10−2 | 8.36 × 10−2 | 2.12 × 10−2 | 14 |
Literacy Rate | 0.063560103 | 6.36 × 10−2 | 8.51 × 10−2 | 2.15 × 10−2 | 15 |
Number of Health care Providers | 0.063792292 | 6.38 × 10−2 | 8.54 × 10−2 | 2.16 × 10−2 | 16 |
Presence of Lifeline | 0.066181942 | 6.62 × 10−2 | 8.86 × 10−2 | 2.24 × 10−2 | 17 |
Road Density | 0.06697054 | 6.70 × 10−2 | 8.96 × 10−2 | 2.27 × 10−2 | 18 |
Female to male ratio | 0.121988782 | 8.07 × 10−2 | 1.22 × 10−1 | 4.13 × 10−2 | 19 |
Poverty Rate | 0.130673052 | 8.64 × 10−2 | 1.31 × 10−1 | 4.43 × 10−2 | 20 |
Dependent Population | 0.232939206 | 1.54 × 10−1 | 2.33 × 10−1 | 7.86 × 10−2 | 21 |
Disabled People | 0.24190139 | 1.60 × 10−1 | 2.42 × 10−1 | 8.20 × 10−2 | 22 |
Unemployed population | 0.27249757 | 1.80 × 10−1 | 2.72 × 10−1 | 9.23 × 10−2 | 23 |
Indicators | Domain |
---|---|
Cropped Area | Exposure |
Number of households | |
Population density | |
Female to male ratio | Sensitivity |
Poverty Rate | |
Dependent Population | |
Disabled People | |
Unemployed population | |
Cyclone shelter | Adaptive Capacity |
Plantation | |
Polder Area | |
Growth Centre | |
GDP | |
Irrigation Equipment | |
Paka and Semi-paka house | |
Loan | |
Communication Infrastructure | |
Cropping intensity | |
Aquaculture | |
Literacy Rate | |
Number of Health care Provider | |
Presence of Lifeline | |
Road Density |
Indicators | Domain | |
---|---|---|
Elimination_1 | Road Density | Adaptive Capacity |
Elimination_2 | Presence of Lifeline | |
Elimination_3 | Number of Health care Provider | |
Elimination_4 | Unemployed Population | Sensitivity |
Indicators | |
---|---|
Cropped Area | Exposure |
Number of households | |
Population density | |
Cyclone shelter | Adaptive Capacity |
Plantation | |
Polder | |
Growth Centre | |
GDP | |
Irrigation Equipment | |
Paka and Semi-paka house | |
Loan | |
Communication Infrastructure | |
Cropping intensity | |
Aquaculture | |
Literacy Rate | |
Female to male ratio | Sensitivity |
Poverty Rate | |
Dependent Population | |
Disabled People |
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Akter, M.; Kabir, R.; Karim, D.S.; Haque, A.; Rahman, M.; Haq, M.A.u.; Jahan, M.; Asik, T.Z. Determining the Most Sensitive Socioeconomic Parameters for Quantitative Risk Assessment. Climate 2019, 7, 107. https://doi.org/10.3390/cli7090107
Akter M, Kabir R, Karim DS, Haque A, Rahman M, Haq MAu, Jahan M, Asik TZ. Determining the Most Sensitive Socioeconomic Parameters for Quantitative Risk Assessment. Climate. 2019; 7(9):107. https://doi.org/10.3390/cli7090107
Chicago/Turabian StyleAkter, Marin, Rubaiya Kabir, Dewan Sadia Karim, Anisul Haque, Munsur Rahman, Mohammad Asif ul Haq, Momtaz Jahan, and Tansir Zaman Asik. 2019. "Determining the Most Sensitive Socioeconomic Parameters for Quantitative Risk Assessment" Climate 7, no. 9: 107. https://doi.org/10.3390/cli7090107