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Urban flood-risk mapping is an important tool for the mitigation of flooding in view of continuing urbanization and climate change. However, many developing countries lack sufficiently detailed data to produce reliable risk maps with existing methods. Thus, improved methods are needed that can help managers and decision makers to combine existing data with more soft semi-subjective data, such as citizen observations of flood-prone and vulnerable areas in view of existing settlements. Thus, we present an innovative approach using the semi-subjective Analytic Hierarchy Process (AHP), which integrates both subjective and objective assessments, to help organize the problem framework. This approach involves measuring the consistency of decision makers’ judgments, generating pairwise comparisons for choosing a solution, and considering criteria and sub-criteria to evaluate possible options. An urban flood-risk map was created according to the vulnerabilities and hazards of different urban areas using classification and regression-tree models, and the map can serve both as a first stage in advancing flood-risk mitigation approaches and in allocating warning and forecasting systems. The findings show that machine-learning methods are efficient in urban flood zoning. Using the city Rasht in Iran, it is shown that distance to rivers, urban drainage density, and distance to vulnerable areas are the most significant parameters that influence flood hazards. Similarly, for urban flood vulnerability, population density, land use, dwelling quality, household income, distance to cultural heritage, and distance to medical centers and hospitals are the most important factors. The integrated technique for both objective and semi-subjective data as outlined in the present study shows credible results that can be obtained without complicated modeling and costly field surveys. The proposed method is especially helpful in areas with little data to describe and display flood hazards to managers and decision makers.

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Despite the advancement of technical tools for the analysis of complex systems, the most important issue in solving water resource problems focuses on the interaction of human and natural systems. Agent-Based Model has been used as an effective tool for the development of integrated human and environmental models. One of the main challenges of this method is identifying and describing the main agents. In this study, three main approach including Genetic Algorithm, cooperative game theory and Agent-Based Model have been used to optimize water allocation in Tajan catchment. The proposed Agent-Based Model is a new equation for calculating stakeholder utility and simulating their interactions that can create a hydrological-environmental-human relationship for demand management and optimal water allocation. The results showed that the total benefit of cooperative game theory and Agent-Based Model relative to Genetic Algorithm has been increased 24 and 21% respectively. Although the total benefit in game theory is greater than the Agent-Based Model, but the Agent-Based Model considering the agents feedback propose a more comprehensive approach to optimal water allocation.

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