Developing Strategies for Urban Flood Management of Tehran City Using SMCDM and ANN

Abstract

Water management in urban areas includes controlling storm water and developing efficient drainage systems. High-intensity rainfall events, reduced permeability attributable to urban development, and the aging of drainage systems are the primary reasons for the occurrence of destructive floods in urban areas. Developing a map of areas with the potential for flood hazard may be an appropriate tool for urban planning and development strategies. The vulnerability analysis of different urban areas is a complex process because it depends on various spatial and temporal parameters and criteria. The purpose of this research is to prepare a tool to make precise decisions in urban flood management by using multicriteria decision making and a geographic information system. The development of an artificial neural network (ANN) model as an alternative to the weighting process of decision makers is presented as a solution to mitigate the disagreement among decision makers on the weighting process of decision-making analysis. The developed spatial multicriteria decision making (SMCDM) tool allows the processing of necessary data and criteria and combining them through the decision-making process. All of the necessary data analysis and processing are automatically run within a developed toolbox. The advantages of using the developed toolbox in generating flood management strategies are discussed in a case study of Tehran, Iran.