Identification areas with inundation potential for urban runoff harvesting using the support vector machine model

     Rainfall-runoff from urban areas is one of the available water resources, which is wasted due to lack of attention and proper management. Besides, urban runoff excess of drains capacity causing many problems including inundation and urban environmental pollution. Therefore, harvesting this runoff can provide a part of the required water in urban areas, and also reduce flood and urban inundation, thus rainwater harvesting systems can play an important role in this regard. The purpose of this study is identification areas with inundation potential for urban runoff harvesting using the support vector machine model. For this purpose, 46 flood points were collected in Imam Ali (AS) town in Mashhad, and 32 points were randomly selected for the model training plus 14 points for the model validation. The predicting variables such as elevation, slope, drainage density, distance from drainage channels, and land use were used to implement the support vector machine model. The results showed that high and very high potential classes for runoff collection include 11.63% and 4.49% of the total area, respectively. The central and north-eastern parts have more potential for urban runoff harvesting, while the western, eastern, and southern regions have the lowest potential for runoff harvesting. Also, the evaluation performance of the support vector machine model was 87.8% in the training stage and 80% in the validation stage, which indicates that the performance of the model is "very good".