Reducing the Vulnerability of Vital Arteries to Flood (Case Study: Sabzevar County)

Subject Areas :

Abolghasem Sanaifard ¹ (PhD Student in Geomorphology, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran.)
Abolghasem Amirahmadi ² (Professor of Geomorphology, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran.)
Yaghoob Zanganeh ³ (Associate Professor of Urban Planning Department, Faculty of Geography and Environmental Sciences, Hakim Sabzevari University, Sabzevar, Iran.)

Received: 2020-11-14 Accepted : 2021-08-13 Published : 2023-04-21

Keywords: flood, Vulnerability, Sabzevar, crisis management, Support Vector Machine (SVM) Model,

Abstract :

As the most devastating natural disasters, floods are often associated with significant loss of life and property. Every year, floods in Iran cause a lot of damage to economic resources. The occurrence of floods and the resulting damage in Iran has been increasing in recent years. The study aims to investigate the risk of floods and assess the damage to Sabzevar city. To study the flood, the support vector machine model was used for flood modeling. After investigating 56 recorded flood points, ten factors influencing floods' occurrence, including land use, population density, canals, slope classes, residential density, CN coefficient, runoff rate, population density, open space, and age of buildings were considered. Then, Justin and ICAR methods were employed to estimate the study area runoff. According to the envelope and Deacon curve, the flood in the region was evaluated in 25-year and 50-year return periods. The results showed that the model's amount of rainfall could be quickly converted into runoff and after routing to yield and water level in the desired sections. The model was evaluated by various accuracy measures, including kappa coefficient, RMSE, system ROC curve performance and prediction rate curve (PRC). The study aims to investigate floods and the factors affecting their occurrence and identify areas prone to floods using the support vector machine (SVM) model to reduce the crisis and vulnerability to urban floods. According to the studies, it was found that the flood dams on the north side of the city do not correspond to the existing problems in Sabzevar city so that the network of urban thoroughfares is more involved in directing urban floods than the network of Sabzevar canals. Therefore, it is necessary to create suitable channels for directing running water and floods in Sabzevar city so that the role of channels in leading floods is more than urban roads. The results of this study can be used to manage flood vulnerable areas and reduce crises.

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