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This research aimed to determine the most appropriate method for formulation the flood susceptibility map in Khlong Nathawi subwatershed, Songkhla Province by using the application of geographic information systems and statistical methods. The methods applied to this study were Frequency Ratio (FR), Artificial Neural Network (ANN) and Multiple Logistic Regression (MLR). Based on the past flooding areas and physical factors affecting flood, 11 factors were considered as: 1) rainfall, 2) mean sea level, 3) slope, 4) soil texture, 5) soil drainage capability, 6) soil depth, 7) distance from drainage, 8) stream network density, 9) road network density, 10) land use and land cover and 11) SCS-CN which are sufficient to contribute the flood susceptibility map. Three methods as FR, ANN and MLR were evaluated to verify the effective of classification accuracy in order to select the most suitable method. According to the result, it revealed that the MLR was the most appropriate method. The factors affecting flood were soil depth (shallow), land use and land cover (aquaculture land, paddy fields), soil drainage capability (slope complex), and soil texture (sandy clay) increased the opportunities for flooding. Meanwhile, the soil texture (sand, fine sandy loam), land use and land cover (orchard, pasture and farm) decreased the opportunities for flooding. Flood susceptibility index values were divided into five classes. They were highest, high, moderate, low and lowest range that covering 5.13%, 3.85%, 3.49%, 6.73% and 80.79% of the areas. To measure the efficiency of the method, it found that the success rate curve and the predictive rate curve were 91.84% and 92.52% respectively.
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