Document Type : Research Paper
Authors
1 Master of Environmental Hazards, Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Iran.
2 Associate Professor, Department of Geomorphology, Faculty of Natural Resources, University of Kurdistan, Iran.
3 Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran.
4 Associate Professor, Department of Rangeland and Watershed Management, Faculty of Natural Resources, University of Kurdistan, Sanandaj, Iran.
Abstract
The flood is the second natural disaster in terms of the damage that it causes to human societies every year. At the same time, cities and population centers are exposed to the highest risk and potential for physical damage caused by flood events. The growing trend of floods in Iran in recent years indicates that most parts of the country are at risk. According to the conducted studies, about forty large and small floods occur annually in different parts of the country. Since the flood is the most devastating disaster in the world and a serious threat to life, preparation of flood hazard maps is essential in identification of flood-sensitive areas, and is one of the first steps taken to reduce damage. It should be noted that researchers can easily identify areas with high risk using flood hazard maps to prevent damage. In the modeling of research on natural and environmental hazards such as floods, the complexity of natural systems makes it difficult to use physical models, highlighting the use of hybrid models as a suitable alternative. Therefore, it is necessary to provide appropriate methods and suggestions for estimation of runoff and flood in areas with high risk in order to prevent their occurrence.
In recent years, a large number of statistical and probabilistic models have been used for flood hazard mapping, and GIS has been used as a basic analysis tool for spatial management and data manipulation due to its capability of managing large amounts of spatial data. Furthermore, it is possible using GIS prediction models to partition urban areas in terms of flood hazard. The obtained hazard maps can be used for identification of areas with high flood hazard. Today, the accuracy of flood susceptibility maps in urban areas can be increased using hybrid GIS models rather than single ones. Therefore, the purpose of this study was to predict the risk of flood in the city of Sanandaj, Kurdistan Province, Iran using hybrid models in the GIS environment. The factors considered in this study as influencing the occurrence of flood included inclination, slope, elevation, distance from the river, river density, flow accumulation, land use, gradient curvature, lithology, distance from the passage, road density, distance from the building, building density, and rainfall. After collecting the required data and layers, we used two algorithms, including the definitive statistical index (SI) and the evidential belief function (EBF), both in isolation and in combination. The locations of flood susceptibility in Sanandaj were predicted. In order to assess the performance of the model, the AUC curve obtained from the ROC curve was used. According to the adopted assessment criteria (ROC) and the validation data, the EBF model (0.840) exhibited better performance than the SI model (0.827) in flood hazard mapping in the area under investigation. The accuracy rate of the hybrid SI-EBF model was 0.849 based on the ROC results, which demonstrated that the hybrid model performed better in prediction of the spatial hazard of flood than the single models. Finally, the results of the study showed that Sanandaj flood spurts resulted from various environmental and human factors, which can be handled using flood hazard maps and appropriate management measures to reduce flood damage.
Keywords
Main Subjects
Bahrami, J., Farooghi, F. and Hosseini, S.A. (2017). Effects of Low-impact Development of Infiltration and Storage Facilities on Urban Runoff Management in City of Sanandaj. Ab wa Fazlab Journal 28, 118- 124. [In Persian].
Bui, D.T., Panahi, M., Shahabi, H., Singh, V.P., Shirzadi, A., Chapi, K., Khosravi, K., Chen, W., Panahi, S. and Li, S. (2018). Novel hybrid evolutionary algorithms for spatial prediction of floods. Scientific reports 8(1), 1-14.
Chapi, K., Singh, V.P., Shirzadi, A., Shahabi, H., Bui, D.T., Pham, B.T. and Khosravi, K. (2017). A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environmental modelling & software 95, 229-245.
Chen, W., Li, Y., Xue, W., Shahabi, H., Li, S., Hong, H., Wang, X., Bian, H., Zhang, S. and Pradhan, B. (2020). Modeling flood susceptibility using data-driven approaches of naïve bayes tree, alternating decision tree, and random forest methods. Science of the Total Environment 701, 134979.
Darvishzadeh, A. (1991) geology of Iran, Sepehr Press. Tehran. [In Persian].
Habibi, K. and Pourahmad, A. (2005). Physical-physical development of Sanandaj city. University of Kurdistan publisher, Kurdistan. [In Persian].
Jebur, M.N., Pradhan, B. and Tehrany, M.S. (2014). Manifestation of LiDAR-derived parameters in the spatial prediction of landslides using novel ensemble evidential belief functions and support vector machine models in GIS. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8(2), 674-690.
Khosravi, K., Pham, B.T., Chapi, K., Shirzadi, A., Shahabi, H., Revhaug, I., Prakash, I. and Bui, D.T. (2018). A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of the Total Environment 627, 744-755.
Khosravi, K., Pourghasemi, H.R., Chapi, K. and Bahri, M. (2016). Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon’s entropy, statistical index, and weighting factor models. Environmental monitoring and assessment 188(12), 656.
Khosravi, K., Shahabi, H., Pham, B.T., Adamowski, J., Shirzadi, A., Pradhan, B., Dou, J., Ly, H.-B., Gróf, G. and Ho, H.L. (2019). A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods. Journal of Hydrology 573, 311-323.
Kia, M.B., Pirasteh, S., Pradhan, B., Mahmud, A.R., Sulaiman, W.N.A. and Moradi, A. (2012). An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environmental earth sciences 67(1), 251-264.
Mohammady, S., Delavar, M. and Pijanowski, B. (2013). Urban growth modeling using ANFIS algorithm: a case study for Sanandaj city, Iran. Int Arch Photogramm Remote Sens Spatial Inf Sci 3, 493-498.
Nampak, H., Pradhan, B. and Abd Manap, M. (2014). Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. Journal of Hydrology 513, 283-300.
Shafapour Tehrany, M., Shabani, F., Neamah Jebur, M., Hong, H., Chen, W. and Xie, X. (2017). GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques. Geomatics, Natural Hazards and Risk 8(2), 1538-1561.
Shafizadeh-Moghadam, H., Valavi, R., Shahabi, H., Chapi, K. and Shirzadi, A. (2018). Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping. Journal of environmental management 217, 1-11.
Shahabi, H., Shirzadi, A., Ghaderi, K., Omidvar, E., Al-Ansari, N., Clague, J.J., Geertsema, M., Khosravi, K., Amini, A. and Bahrami, S. (2020). Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble based on k-nearest neighbor classifier. Remote Sensing 12(2), 266.
Stefanidis, S. and Stathis, D. (2013). Assessment of flood hazard based on natural and anthropogenic factors using analytic hierarchy process (AHP). Natural hazards 68(2), 569-585.
Tehrany, M.S., Lee, M.-J., Pradhan, B., Jebur, M.N. and Lee, S. (2014a). Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environmental earth sciences 72(10), 4001-4015.
Tehrany, M.S., Pradhan, B. and Jebur, M.N. (2013). Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology 504, 69-79.
Tehrany, M.S., Pradhan, B. and Jebur, M.N. (2014b). Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology 512, 332-343.
Tehrany, M.S., Pradhan, B., Mansor, S. and Ahmad, N. (2015). Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena 125, 91-101.
Tien Bui, D., Khosravi, K., Shahabi, H., Daggupati, P., Adamowski, J.F., Melesse, A.M., Thai Pham, B., Pourghasemi, H.R., Mahmoudi, M. and Bahrami, S. (2019). Flood spatial modeling in northern Iran using remote sensing and gis: A comparison between evidential belief functions and its ensemble with a multivariate logistic regression model. Remote Sensing 11(13), 1589.
Wang, Y., Hong, H., Chen, W., Li, S., Panahi, M., Khosravi, K., Shirzadi, A., Shahabi, H., Panahi, S. and Costache, R. (2019). Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm. Journal of environmental management 247, 712-729.
Wheater, H. and Evans, E. (2009). Land use, water management and future flood risk. Land use policy 26, S251-S264.
Youssef, A.M., Pradhan, B. and Sefry, S.A. (2016). Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. Environmental earth sciences 75(1), 12.
Zou, Q., Zhou, J., Zhou, C., Song, L. and Guo, J. (2013). Comprehensive flood risk assessment based on set pair analysis-variable fuzzy sets model and fuzzy AHP. Stochastic Environmental Research and Risk Assessment 27(2), 525-546.
)