نوع مقاله : پژوهشی
نویسندگان
1 گروه مرتع و آبخیزداری، دانشکده منابع طبیعی، دانشگاه ارومیه، ارومیه، ایران
2 دانشیار دانشکده منابع طبیعی دانشگاه ارومیه
3 بخش تحقیقات حفاظت خاک و آبخیزداری، مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی کردستان، سازمان تحقیقات آموزش و ترویج کشاورزی، سنندج،
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
Introduction and Objectives
Floods cause significant financial losses and loss of life in the country every year. Although information on the location of flood events is of high scientific value, in many flood studies, flood zones have been determined solely based on expert’s opinion and multi-criteria decision-making methods. The aim of this study is to predict the spatial pattern of flood susceptibility in the Sirwan watershed of Kurdistan province using spatial information on flood events in the last decade. In the present study, support vector machine (SVM), which is a machine learning-based model, has been used to achieve this goal.
Materials and Methods
In order to carry out this research, first a database in the geographic information system was prepared for flooding events in the Sirwan watershed using bank flood data from the Main Directorate of Natural Resources and Watershed Management of Kurdistan Province. Since machine learning models require points of occurrence and non-occurrence of flooding, points of non-occurrence of flooding were also selected based on the homogeneous-unit method. This database was completed using information obtained from face-to-face interviews with local communities in this area. Based on the various characteristics of the Sirwan watershed and a review of scientific sources, sixteen factors affecting flooding events were selected and their digital maps were prepared. Factors affecting flooding included elevation, slope aspect, slope percentage, convergence index, drainage density, land use, maximum 24-hour precipitation, number of rainfalls higher than the average of the meteorological station, normalized vegetation difference index, plan curvature, profile curvature, soil texture, distance from the stream, topographic position index, topographic wetness index, and vertical distance from the channel, which were used as independent variables in the modeling. Flooding occurrence and non-occurrence data were randomly divided into two training and validation groups with proportions of 70% and 30%. After implementing the support vector machine model in the R software environment, a flood susceptibility map of the Sirwan watershed was prepared and the spatial pattern of flood susceptibility was examined. The accuracy of the map was evaluated using the area under the curve (AUC) statistic of the receiver operating characteristic.
Results and Discussion
After validation, the results showed that the support vector machine model with an area under the receiver operating characteristic curve (AUC) of 0.921 (92.1%) has a high capability to predict flood-prone areas. Given that the model's prediction accuracy is reported to be more than 90%, based on the common classification of model efficiency, the performance of the support vector machine model in the Sirwan watershed is considered excellent. Based on the analyses, the very low, low, medium, high, and very high flood susceptibility classes include 51, 10, 17, 20, and 2 percent of the Sirwan watershed, respectively. Given the identification of flood-prone areas, the implementation priorities for the flood management plan have been clearly identified for implementing measures. Out of the 127 catchments of the Sirwan watershed, 15 catchments are in the high flood-prone class and 8 catchments are in the very high flood-prone class. All catchments located in the high talent class (such as Gazerkhani, Palangan, Shwishe, Sawji, Sleen, Sianaw, Danan, Ghshlagh, Zrivar, etc.) and catchments located in the very high talent class (such as Sanandaj, Babarez, Marivan, Gholian, Mochesh, Doulbakh, Doroud, and Khamsan) have a relatively high population density.
Conclusions and Suggestions
Based on the results of this study, the support vector machine model has a very good performance in identifying areas prone to flooding, which is important in the management and planning of watershed programs, given the lack of specialized data and financial resources in the executive agencies. Prioritization of executive catchments was done based on the severity of flood inundation, which in conditions of data shortage, while saving time and resources, will also improve the effectiveness of watershed management plans. On this basis, it can be suggested that the support vector machine model be used at higher provincial, regional and national levels, as well as in detailed-implementation studies of watershed management and risk management.
کلیدواژهها [English]
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