Flood Hazard Zoning in Watershed Scale using Fuzzy Logic (Case study: Akhtar Abad Watershed)

Introduction
Flood, as one of natural disasters, causes considerable damages in almost all parts of the world including Iran. Therefore, watershed prioritization is a practical useful tool which provides relevant information in the flood prevention planning. Floods cause severe damages in terms of both natural environments and human lives, making the hydrologists and water resources managers to be concerned with estimating the potential risk associated with these events. Recent trends in storm-water best management practices are aimed at finding and focusing on high potential flood generation in parts of a watershed. In order to prevent flood occurrence, it is essential to find out the areas of the watershed where the potential of runoff generation is high and how to use and incorporate available information to improve prioritization process. Such a process is vital to support the decision making and watershed monitoring, modeling, and management. It also helps in reducing the set up and running cost and improves efficiency. For example, it is usually time consuming and costly to set up a monitoring station in each sub-basin when a watershed consists of a large number of sub-basins. For this purpose, Akhtar Abad Watershed was chosen as a case study for flood potential zoning based on the Fuzzy Analytical Hierarchy Process (FAHP). The watershed has an area of 30785.28 ha which is located in the southwestern part of Alborz province and northern part of Markazi province. Over the last years, floods have resulted severe damage to the infrastructure and human life losses in this region.
Methodology
The analytic hierarchy process (AHP) is one of the most commonly used methods of assessment which works on a premise that decision making of complex problems can be handled by structuring the complex problem into a simple and comprehensible hierarchical structure. Despite of its wide range of applications, the conventional AHP approach may not fully reflect a style of human thinking, in which human’s judgments are represented as exact numbers. However, in many practical situations, decision makers usually feel more confident to give interval judgments rather than expressing their judgments in the form of exact numeric values. Therefore, AHP technique involves subjectivity in pair-wise comparisons and vagueness and uncertainty dominate in this process. Combining AHP into the fuzzy system brings the triangular fuzzy number of the fuzzy set theory directly into the pair-wise comparison matrix of the AHP. The purpose is to solve vague problems, which occur during the analysis of criteria and the judgment process. FAHP should be able to tolerate vagueness or ambiguity, and should thus be more appropriate and effective than conventional AHP in real practice. In order to find out the most relevant factor in flood occurrence potential, a literature review was conducted and the suggested criteria was adjusted based on the expert's ideas and geographical facts of the region. Then, each criterion was weighted based on a paired questionnaire and Fuzzy AHP approach. A fuzzy set is a class of objects with continuous grades of membership which represents the degree of truth as an extension of valuation. Fuzzy sets generalize classical sets while the indicator functions of these sets are special cases of the membership functions of fuzzy sets for the latter only take values 0 or 1.This classification approach is able to categorize the sub- basins and identify the ones which can sufficiently represent the watershed characteristics in a reduced number in terms of their runoff generation potential. A fuzzy set operation is an operation on fuzzy sets, which are a generalization of crisp set operations. The most widely used operations are called standard fuzzy set operations, which include unions, complements, and intersections. Fuzzy set theory has been introduced into classification to help reflect uncertain information. Then Fuzzy functions were considered for spatial modeling and zoning based on the membership function of each factor. Finally, the most prone areas of the watershed to flooding were mapped.
In order to find out the potential areas, weights of each factor and sub factors were determined based on questionnaire and membership function of each responsible factor. Then fuzzy map of the each factor was incorporated to have the flood zoning map based on the most susceptible fuzzy operator. In this way, the final map was classified into seven classes based on standard deviation.

Results and Discussion
The developed FAHP system in this research is able to meet the needs of more efficient and reliable approaches for watershed classification to deal with complex and uncertain features. The results showed that the most prone areas are located in the north and south of the region while the most part of the region is in the class 4 of moderate flooding potential. The overlaid maps showed that the most susceptible areas are mostly located in areas of more than 60 percent of slope and annual precipitation range of 300-400 mm.
Conclusion
Watershed prioritization is the ranking of different sub-watersheds according to the order in which they have to be taken for treatment and flood control measures. According to the resulted map of flooding potential, the most prone areas are located in the north and south of the region where land slope is relatively high which increases runoff travel time and decrease time of concentration. Meanwhile, increased precipitation means higher potential of flooding in terms of higher runoff potential. Application of Analytical Hierarchy Process with Fuzzy logic shows better adjustment with human explanation of the environment and could provide better and flexible results compared to other overlaying approaches.