A Spatial Fuzzy Compromise Approach for Flood Disaster Management

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. Floodplain 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. Some 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. In 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. Through 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.https://ir.lib.uwo.ca/wrrr/1004/thumbnail.jp

Analysis of LiDAR point data and derived elevation models for mapping and characterizing bouldery landforms

This thesis assessed the viability of using LiDAR-derived elevation data in accurately mapping and characterizing bouldery geomorphic features in a study area in the Allegheny Mountains. This study showed that the ground returns classification process conducted by the Canaan Valley Institute (CVI) for their property using the TerraScan software generally removed 5 to 10 m scale local topographic variability and bouldery landforms in creating the CVI classified ground returns data. In open areas, last returns elevation and intensity data were successfully used in this study to map bouldery landforms in the study area. Identifying and describing boulders under a tree canopy required a relatively reliable ground classification of LiDAR points. This study\u27s classifications conducted within Prologic LiDAR Explorer provided a more useful representation than the CVI classified ground data for mapping bouldery landforms and generalized rugged topography. Index overlay for likelihood of presence of bouldery landforms using supervised classified aerial imagery and LiDAR-derived parameters in a raster environment was explored as an alternative means of detecting bouldery landforms because hillshade imagery derived from CVI classified ground data were inadequate for mapping bouldery landforms

GIS-based multicriteria analysis as decision support in flood risk management

In this report we develop a GIS-based multicriteria flood risk assessment and mapping approach. This approach has the ability a) to consider also flood risks which are not measured in monetary terms, b) to show the spatial distribution of these multiple risks and c) to deal with uncertainties in criteria values and to show their influence on the overall assessment. It can furthermore be used to show the spatial distribution of the effects of risk reduction measures. The approach is tested for a pilot study at the River Mulde in Saxony, Germany. Therefore, a GISdataset of economic as well as social and environmental risk criteria is built up. Two multicriteria decision rules, a disjunctive approach and an additive weighting approach are used to come to an overall assessment and mapping of flood risk in the area. Both the risk calculation and mapping of single criteria as well as the multicriteria analysis are supported by a software tool (FloodCalc) which was developed for this task. --

Spatially explicit multi-criteria decision analysis for managing vector-borne diseases

The complex epidemiology of vector-borne diseases creates significant challenges in the design and delivery of prevention and control strategies, especially in light of rapid social and environmental changes. Spatial models for predicting disease risk based on environmental factors such as climate and landscape have been developed for a number of important vector-borne diseases. The resulting risk maps have proven value for highlighting areas for targeting public health programs. However, these methods generally only offer technical information on the spatial distribution of disease risk itself, which may be incomplete for making decisions in a complex situation. In prioritizing surveillance and intervention strategies, decision-makers often also need to consider spatially explicit information on other important dimensions, such as the regional specificity of public acceptance, population vulnerability, resource availability, intervention effectiveness, and land use. There is a need for a unified strategy for supporting public health decision making that integrates available data for assessing spatially explicit disease risk, with other criteria, to implement effective prevention and control strategies. Multi-criteria decision analysis (MCDA) is a decision support tool that allows for the consideration of diverse quantitative and qualitative criteria using both data-driven and qualitative indicators for evaluating alternative strategies with transparency and stakeholder participation. Here we propose a MCDA-based approach to the development of geospatial models and spatially explicit decision support tools for the management of vector-borne diseases. We describe the conceptual framework that MCDA offers as well as technical considerations, approaches to implementation and expected outcomes. We conclude that MCDA is a powerful tool that offers tremendous potential for use in public health decision-making in general and vector-borne disease management in particular

Fuzzy Set Ranking Methods and Multiple Expert Decision Making

The present report further investigates the multi-criteria decision making tool named Fuzzy Compromise Programming. Comparison of different fuzzy set ranking methods (required for processing fuzzy information) is performed. A complete sensitivity analysis concerning decision maker’s risk preferences was carried out for three water resources systems, and compromise solutions identified. Then, a weights sensitivity analysis was performed on one of the three systems to see whether the rankings would change in response to changing weights. It was found that this particular system was robust to the changes in weights. An inquiry was made into the possibility of modifying Fuzzy Compromise Programming to include participation of multiple decision makers or experts. This was accomplished by merging a technique known as Group Decision Making Under Fuzziness, with Fuzzy Compromise Programming. Modified technique provides support for the group decision making under multiple criteria in a fuzzy environment.https://ir.lib.uwo.ca/wrrr/1001/thumbnail.jp

Pertumbuhan dan Hasil Gandum pada Berbagai Kerapatan Populasi dan Dosis Pemupukan Urea

Efforts to produce wheat in Indonesia need to be supported by the availability of cultivation technology suitable for agro-climate conditions in Indonesia, including the use of proper population density and fertilization dosages, which are fundamental for obtaining maximum yields. This study was aimed to determine ideal population density and dosage of urea for optimum grain yield of wheat in Lombok Island. The experiment carried out at Aik Bukak, Central Lombok at elevation of 400 m asl, to observe the growth and yield of two varieties of wheat, Nias and Gladius, with 4 plant population densities (133, 160, 200, and 250 plants m-2) and 3 dosages of urea fertilization, 200, 300, and 400 kg ha-1. Data from this study indicated that Lombok Island has the potential for growing wheat with quite dense population density of 250 plants m-2. This density yielded higher, 1.74 tons ha-2, due to the increased number of kernel per unit area without reducing individual kernel weight. The use of urea 300 kg ha-1 exhibited the best growth and yielded 1.32 tons ha-2.Keywords: fertilization, population density, wheat varietie

Local Ideal Point Method for GIS-based Multicriteria Analysis: A Case Study in London, Ontario

GIS-based multicriteria analysis (GIS-MCA) is a procedure for transforming and combining geographic data and value judgments (preferences) to evaluate a set of alternatives with respect to relevant criteria. Ideal Point Method (IPM) is one of the most often used GIS-MCA techniques. It has been applied in many research/planning areas including environmental planning, urban/regional planning, waste management, water resource management and agriculture. One of the limitations of IPM is that it has conventionally been used as a global approach based on the implicit assumption that its parameters do not vary as a function of geographic space. The conventional IPM assumes a spatial homogeneity of its parameters within the whole study area. This thesis proposes a new IPM called local IPM. The local version of IPM is developed by localizing two parameters (criterion weights and ideal/nadir points) based on the range-sensitivity principle. The IPM methods are used to evaluate and analyse the spatial patterns of the quality of employment in London, Ontario. The case study shows that there are significant differences between the spatial patterns generated by the local and conventional IPM. The local IPM not only can display the general ‘spatial trend’ of the quality of employment in London, but also is able to highlight the areas with relatively high quality of employment in different neighborhoods. Furthermore, the local IPM provides a tool for visualizing and exploring spatial patterns. The parameters influencing the local IPM results can be mapped and further examined with GIS

A decision support tool for flood management under uncertainty using gis and remote sensing technology

Proceedings of the Seventh International Conference on Hydroscience and Engineering, Philadelphia, PA, September 2006. http://hdl.handle.net/1860/732Historical flood events have shown that the level of damage does not solely depend on exposure to flood waters. Vulnerabilities due to various socio-economic factors such as population at risk, public awareness, and presence of early warning systems, etc. should also be taken into account. Federal and state agencies, watershed management coalitions, insurance companies, need reliable decision support tools to evaluate flood risk, to plan and design flood management and mitigation systems. In current practice, flood damage evaluations are generally carried out based on results obtained from one dimensional (1D) numerical simulations. In some cases, however, 1D simulation is not able to accurately capture the dynamics of the flood events. The present study describes a decision support tool, which is based on 2D flood simulation results obtained with CCHE2D-FLOOD. The 2D computational results are complemented with information from various resources, such as census block layer, detailed survey data and remote sensing images, to estimate loss-of-life and direct damages (meso or micro scale) to property under uncertainty. Flood damage calculations consider damages to residential, commercial and industrial buildings in urban areas, and damages to crops in rural areas. The decision support tool takes advantage of fast raster layer operations in a GIS platform to generate flood hazard maps based on various user-defined criteria. Monte Carlo method based on an event tree analysis is introduced to account for uncertainties in various parameters. A case study illustrates the uses of the proposed decision support tool. The results show that the proposed decision support tool allows stake holders to have a better appreciation of the consequences of the flood. It can also be used for planning, design and evaluation of future flood mitigation measures