A Spatial Fuzzy Compromise Approach for Flood Disaster Management
Bibliographic record
Abstract
Natural disasters affect regions with different intensity and produce damages that vary in space. Topographical features of the region; location of properties that may be exposed to the peril; level of exposure; impact of different mitigation measures; are all variables with considerable spatial variability. A new method for evaluation of disaster impacts has been presented in this report that takes into consideration spatial variability of variables involved and associated uncertainty. Flood management has been used to illustrate the utility of proposed approach.\nFloodplain management is a spatial problem. Representation of flood damage mitigation alternatives and objectives in space provides a better insight into the management problem and its characteristics. Protection of a region from floods can be achieved through various structural and non-structural measures. Comparison of different measures and evaluation of their impacts is based on the multiple criteria. If they are described spatially, decision-making problem can be conceptualized as spatial multi criteria decision-making (MCDM). Tkach and Simonovic (1997) introduced spatial Compromise Programming (SPC) technique to account for spatial variability in flood management.\nSome of the criteria and preferences of the stakeholders involved with flood management are subject to uncertainty that may originate in the data, knowledge of the domain or our ability to adequately describe the decision problem. The main characteristic of flood management is the existence of objective and subjective uncertainty. Fuzzy set theory has been successfully used to address both, objective and subjective uncertainty. Bender and Simonovic (2000) incorporated vagueness and imprecision as sources of uncertainty into multi criteria decision-making in water resources.\nIn this report a new technique named Spatial Fuzzy Compromise Programming (SFCP) has been developed to enhance our ability to address the issues related to uncertainties in spatial environment. A general fuzzy compromise programming technique, when made 2 spatially distributed, proved to be a powerful and flexible addition to the list of techniques available for decision making where multiple criteria are used to judge multiple alternatives. All uncertain variables (subjective and objective) are modeled by way of fuzzy sets. In the present study, fuzzy measures have been introduced to spatial multi criteria decision-making in the GIS environment in order to account for uncertainties.\nThrough a case study of the Red River floodplain near the City of St. Adolphe in Manitoba, Canada, it has been illustrated that the new technique provides measurable improvement in flood management. Final results in the form of maps that shown spatial distribution of the impacts of mitigation measures on the region can be of great value to insurance industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".