A review of modelling methodologies for flood source area (FSA) identification

Flooding is an important global hazard that causes an average annual loss of over 40 billion USD and affects a population of over 250 million globally. The complex process of flooding depends on spatial and temporal factors such as weather patterns, topography, and geomorphology. In urban environments where the landscape is ever-changing, spatial factors such as ground cover, green spaces, and drainage systems have a significant impact. Understanding source areas that have a major impact on flooding is, therefore, crucial for strategic flood risk management (FRM). Although flood source area (FSA) identification is not a new concept, its application is only recently being applied in flood modelling research. Continuous improvements in the technology and methodology related to flood models have enabled this research to move beyond traditional methods, such that, in recent years, modelling projects have looked beyond affected areas and recognised the need to address flooding at its source, to study its influence on overall flood risk. These modelling approaches are emerging in the field of FRM and propose innovative methodologies for flood risk mitigation and design implementation; however, they are relatively under-examined. In this paper, we present a review of the modelling approaches currently used to identify FSAs, i.e. unit flood response (UFR) and adaptation-driven approaches (ADA). We highlight their potential for use in adaptive decision making and outline the key challenges for the adoption of such approaches in FRM practises

Data-Driven Mathematical Modeling and Global Optimization Framework for Entire Petrochemical Planning Operations

In this work we develop a novel modeling and global optimization-based planning formulation, which predicts product yields and properties for all of the production units within a highly integrated refinery-petrochemical complex. Distillation is modeled using swing-cut theory, while data-based nonlinear models are developed for other processing units. The parameters of the postulated models are globally optimized based on a large data set of daily production. Property indices in blending units are linearly additive and they are calculated on a weight or volume basis. Binary variables are introduced to denote unit and operation modes selection. The planning model is a large-scale non-convex mixed integer nonlinear optimization model, which is solved to e-global optimality. Computational results for multiple case studies indicate that we achieve a significant profit increase (37-65%) using the proposed data-driven global optimization framework. Finally, a user-friendly interface is presented which enables automated updating of demand, specification, and cost parameters. (C) 2016 American Institute of Chemical Engineers</p