Miyandoab flood risk mapping using dematel and SAW methods and DPSIR model

Safaripour, Mahsa; Rezapour Andabili, Nafiseh

doi:10.22104/aet.2021.4766.1287

Miyandoab flood risk mapping using dematel and SAW methods and DPSIR model

Document Type : Research Paper

Authors

¹ Department of Agriculture and Natural Resources Engineering, Payam Noor University, Tehran, Iran

² Malayer University, Malayer, Iran

10.22104/aet.2021.4766.1287

Abstract

It is essential to assess flood risk mapping for sustainable development. The present study aimed to identify the causes of flooding and predict the extent of damage caused in the area of Miyandoab, Iran. The Driver, Pressure, State, Impacts, response (DPSIR) conceptual framework model was used to analyze the factors affecting flooding in the region. DPSIR is a system approach that identifies key relationships between humans and the environment, and its combination with the simple additive weighting (SAW) model identifies a new strategy for achieving sustainable development. The DPSIR method for flood susceptibility analysis in the region, examined the social, economic, physical, and environmental factors as the driving force. The flood risk level was then determined for the region by preparing the driving force map and mapping the region. For this purpose, the decision-making trial and evaluation laboratory (Dematel) and SAW models were used to investigate the causal relationship between the factors and calculate the weight of layers; Matlab software was used to implement the models. Finally, based on the weights extracted from the SAW method, risk mapping was performed in the geographic information system (GIS) environment. The results showed that out of the total area of the study area, about 78,462 hectares have a high risk, 91,542 hectares have medium risk, and 2,952 hectares have a low risk of flood. The results from combining the models of decision support systems and GIS indicated high efficiency in determining the areas with a high risk of flood.

Keywords

Main Subjects

modeling and simulation

References

[1] ThaiPham, B., Luu, C., VanDao, D., Phong, T., Nguyen, H., Le, H., Meding, J., Prakash, I. (2021). Flood risk assessment using deep learning integrated with multi-criteria decision analysis. Knowledge-based systems, 219, 1-10.

[2] Dale, M. (2021). Managing the effects of extreme sub-daily rainfall and flash floods—a practitioner's perspective. Philosophical transactions of the royal society, 379, 7-16.

[3] León‚ J., March‚ A. (2014). Urban morphology as a tool for supporting tsunami rapid resilience: A case study of Talcahuano, Chile. Habitat international‚ 43, 250-262.

[4] Mishra, K., Sinha, R. (2020). Flood risk assessment in the Kosi megafan using multi-criteria decision analysis: A hydro-geomorphic approach. Geomorphology, 350, 106861.

[5] Medeiros, E., Zwet, A. (2020). Evaluating integrated sustainable urban development strategies: a methodological framework applied in Portugal. European planning studies, 28 (3), 563-582.

[6] Associated Programme on Flood Management (APFM). (2008). Urban flood risk management: A tool for integrated flood management. World meteorological organization (WMO), 44.

[7] Taghvaei, M., Soleimani, F. (2011). Urban crisis management with emphasis on flood. Sepehr, 20 (79), 66-73.

[8] Maantay, J., Maroko, A. (2009). Mapping urban risk: Flood hazards, race andenvironmental justice in New York. Applied geography, 29(3), 111–124.

[9] Bheshem, R., Serwan, M. (2008). Developing a GIS based integrated approach to flood management in Trinidad, West Indies. Environmental management, 88(4), 1131-1140.

[10] Plate, E. (2002). Flood risk and flood management. Journal of hydrology ,267(8), 2-11.

[11] Jonkman, S. N. (2005). Global perspectives on loss of human life caused by floods. Natural hazards, 34(2), 151-175.

[12] Skoulikidis, N. (2009). The environmental state of rivers in the Balkans: A review within the DPSIR framework. Science of the total environment, 407(5), 2501-2516.

[13] Hammond, M., Chen, A., Batica, J., Butler, D., Djordjevic, S., Gourbesville, P., Manojlovic, N., Mark, O., Veerbeek, W. (2018). A new flood risk assessment framework for evaluating the effectiveness of policies to improve urban flood resilience. Urban water journal, 15(5), 427-436.

[14] Wang, W., Sun, Y., Wu, J. (2018). Environmental warning system based on the DPSIR model: a practical and concise method for environmental assessment. Sustainability, 10(6), 1728.

[15] Sepehri, M., Malekinezhad, H., Jahanbakhshi, F., Ildoromi, A. R., Chezgi, J., Ghorbanzadeh, O., Naghipour, E. (2020). Integration of interval rough AHP and fuzzy logic for assessment of flood prone areas at the regional scale. Acta geophysica, 68(2), 477-493.

[16] Chakraborty, S., Mukhopadhyay, S. (2019). Assessing flood risk using analytical hierarchy process (AHP) and geographical information system (GIS): application in Coochbehar district of West Bengal, India. Natural hazards, 99(1), 247-274.

[17] Manafiazar, R., Valaei, M. (2019). Comparative Analysis of Inequalities in Urban Space and Urban Peripheral Spaces, Case: Miyandoab. Journal of urban peripheral development, 1 (1), 111 – 128.

[18] Zare, F., Elsawah, S., Bagheri, A., Nabavi, E., Jakeman, A. J. (2019). Improved integrated water resource modelling by combining DPSIR and system dynamics conceptual modelling techniques. Journal of environmental management, 246, 27-41.

[19] Semeoshenkova, V., Newton, A., Rojas, M., Piccolo, M., Bustos, M., Cisneros, M., Berninsone, L. (2017). A combined DPSIR and SAF approach for the adaptive management of beach erosion in Monte Hermoso and Pehuen Co (Argentina). Ocean and coastal management, 143, 63-73.

[20] Wang, P., Zhu, Z., Wang, Y. (2016). A novel hybrid MCDM model combining the SAW, TOPSIS and GRA Methods Based on experimental design. Information sciences, 345, 27–45.

[21] Razmjoo, AA., Sumper, A., Davarpanah, A. (2019). Development of sustainable energy indexes by the utilization of new indicators: A comparative study. Energy reports, 5, 375-383.

[22] Razmjoo, AA., Sumper, A., Davarpanah, A. (2019). Energy sustainability analysis based on SDGs for developing countries. Energy sources, part A: recovery, utilization and environmental effects, 42, 1041-1056.