Identifying Waterlogging Risk Zones in an Urban Area of Bangladesh Using AHP-Fuzzy Rule Based Approach

Waterlogging is a very common problem during the rainy season in Bangladesh, which often causes physical, social, and economic losses in both urban and rural areas. The problem is more severe, particularly in the urban areas of the country. Khulna City Corporation (KCC), being the third-largest city of the country, is located on a low-lying area on the west bank of the Rupsha River. The city has been suffering from water logging problems due to the rapid growth of the city and urbanization, the disruption of natural drainage system, high-intensity rainfall, increased population, an unplanned urban drainage system, the siltation of nearby river beds, etc. and so on. Therefore, the aim of the current study is to explore the waterlogging situation and identify waterlogging risk zones in the KCC. At first, social, economic, and hydrological data were collected from the Bangladesh Meteorological Department (BMD), the Bangladesh Bureau of Statistics (BBS), and KCC data sources. Various hydrological data such as inundation depth, drainage density, slope, etc. have been processed from the digital elevation model (DEM) through spatial analysis in the geographic information system (GIS) platform. After data collection and processing, several indicators are identified, and a criteria weight for each indicator is obtained by the analytical hierarchy process (AHP) technique. Then a fuzzy inference system (FIS) was developed in the MATLAB platform to determine the vulnerability index of three factors, such as social, economic, and hydrological. After determining the vulnerability index of three factors, a final water logging vulnerability index was determined. Finally, the waterlogging risk zones were delineated based on the index, which was interpreted by the GIS mapping. From the analysis, it is found that about 20% of the KCC area lies in the high water logging risk zone, about 42% of the KCC area is in the medium water logging risk zone, and the remaining areas cover the low water logging risk zones. Overall, this study demonstrates that the integrated AHP-fuzzy rule based approach can be effectively used for identifying waterlogging risk zones.