Paper:
Terrain Hazard Risk Analysis for Flood Disaster Management in Chaohu Basin, China, Based on Two-Dimensional Cloud
Jun Lyu*,, Xianfu Cheng*, and Peter Shaw**
*School of Geography and Tourism, Anhui Normal University
No.189 South Jiuhua Road, Yijiang District, Wuhu, Anhui 241003, China
**School of Engineering and Information Technology, Charles Darwin University
Ellengowan Drive, Darwin, Northern Territory 0909, Australia
Corresponding author
Terrain analysis is essential to flood disaster risk evaluation. It is a complicated evaluation process, involving both quantitative and qualitative data analysis. However, quantitative and qualitative data cannot be put into operation directly. Based on stochastic and fuzzy mathematics, cloud models allow interchange between qualitative and quantitative data, dealing with randomness and ambiguity. Two- or multi-dimensional cloud models can solve the problem of multivariable analysis. This study used absolute elevation and neighborhood elevation standard deviation as main factors. Using the model, it demonstrated the construction of qualitative conditions and risk evaluation clouds and established a set of two-dimensional cloud reasoning rules to calculate the joint certainties with all the grids in reasoning rules. By selecting the highest certainty of cloud reasoning, preliminary evaluation results were obtained. For more accurate results, the model algorithm was improved, and further iterations were performed. The results of two-dimensional cloud reasoning showed better dispersion and precision than traditional methods did. The terrain risk distribution of Chaohu Basin, China, agreed with reality with great detail. A new method regarding the risk assessment of flood disaster was also proposed.
- [1] EM-DAT Disaster Profiles Website, http://www.emdat.be/database [accessed August 20, 2019]
- [2] R. Rojas, L. Feyen, and P. Watkiss, “Climate change and river floods in the European Union: Socio-economic consequences and the costs and benefits of adaptation,” Global Environmental Change, Vol.23, Issue 6, pp. 1737-1751, 2013.
- [3] S.-K. Min, X. Zhang, F. W. Zwiers, and G. C. Hegerl, “Human contribution to more-intense precipitation extremes,” Nature, Vol.470, pp. 378-381, 2011.
- [4] E. E. Koks, B. Jongman, T. G. Husby, and W. J. W. Botzen, “Combining hazard, exposure and social vulnerability to provide lessons for flood risk management,” Environmental Science & Policy, Vol.47, pp. 42-52, 2015.
- [5] S. Stefanidis and D. Stathis, “Assessment of flood hazard based on natural and anthropogenic factors using analytic hierarchy process (AHP),” Natural Hazards, Vol.68, Issue 2, pp. 569-585, 2013.
- [6] V. Norén, B. Hedelin, L. Nyberg, and K. Bishop, “Flood risk assessment – Practices in flood prone Swedish municipalities,” Int. J. of Disaster Risk Reduction, Vol.18, pp. 206-217, 2016.
- [7] S. P. Ozkan and C. Tarhan, “Detection of Flood Hazard in Urban Areas Using GIS: Izmir Case,” Procedia Technology, Vol.22, pp. 373-381, 2016.
- [8] Y. Jung, Y. Shin, C. H. Jang et al., “Estimation of flood risk index considering the regional flood characteristics: a case of South Korea,” Paddy Water Environment, Vol.12, pp. 41-49, 2014.
- [9] S. N. Jonkman, “Global Perspectives on Loss of Human Life Caused by Floods,” Natural Hazards, Vol.34, Issue 2, pp. 151-175, 2005.
- [10] S. N. Jonkman, M. Kok, and J. K. Vrijling, “Flood Risk Assessment in the Netherlands: A Case Study for Dike Ring South Holland,” Risk Analysis, Vol.28, Issue 5, pp. 1357-1374, 2008.
- [11] S. Sharma S. V., P. S. Roy, Chakravarthi V., and S. Rao G., “Flood risk assessment using multi-criteria analysis: a case study from Kopili river basin, Assam, India,” Geomatics, Natural Hazards and Risk, Vol.9, Issue 1, pp. 79-93, 2018.
- [12] H. Mojaddadi, B. Pradhan, H. Nampak et al., “Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS,” Geomatics, Natural Hazards and Risk, Vol.8, Issue 2, pp. 1080-1102, 2017.
- [13] C. Lai, X. Chen, X. Chen et al., “A fuzzy comprehensive evaluation model for flood risk based on the combination weight of game theory,” Natural Hazards, Vol.77, Issue 2, pp. 1243-1259, 2015.
- [14] D. Alvarado-Aguilar, J. A. Jiménez, and R. J. Nicholls, “Flood hazard and damage assessment in the Ebro Delta (NW Mediterranean) to relative sea level rise,” Natural Hazards, Vol.62, Issue 3, pp. 1301-1321, 2012.
- [15] R. Kumar, “Flood hazard assessment of 2014 floods in Sonawari sub-district of Bandipore district (Jammu & Kashmir): An application of geoinformatics,” Remote Sensing Applications: Society and Environment, Vol.4, pp. 188-203, 2016.
- [16] Y.-R. Chen, C.-H. Yeh, and B. Yu, “Integrated application of the analytic hierarchy process and the geographic information system for flood risk assessment and flood plain management in Taiwan,” Natural Hazards, Vol.59, Issue 3, pp. 1261-1276, 2011.
- [17] A. M. Dewan, M. M. Islam, T. Kumamoto, and M. Nishigaki, “Evaluating Flood Hazard for Land-Use Planning in Greater Dhaka of Bangladesh Using Remote Sensing and GIS Techniques,” Water Resources Management, Vol.21, Issue 9, pp. 1601-1612, 2007.
- [18] X. Xue, J. Ma, and H. Li, “Risk Assessment and Dividing Technology of Flood Disasters in Villages and Towns Based on GIS – A Case Study of Linzi District in Zibo City of Shandong Province,” J. of Catastrophology, Vol.27, No.4, pp. 71-74+91, 2012 (in Chinese).
- [19] G. Yin and H. Wei, “Cloud computing: a method to realize conceptual computing,” J. of Southeast University (Natural Science Edition), Vol.33, No.4, pp. 502-506, 2003 (in Chinese).
- [20] D. Li, “Uncertainty in Knowledge Representation,” Engineering Science, Vol.2, No.10, pp. 73-79, 2000 (in Chinese).
- [21] D. Li, K. Di, D. Li, and X. Shi, “Mining association rules with linguistic cloud models,” X. Wu, R. Kotagiri, and K. B. Korb (Eds.), “Research and Development in Knowledge Discovery and Data Mining: 2nd Pacific-Asia Conf., PAKDD-98, Melbourne, Australia, April 15-17, 1998 Proc.,” pp. 392-393, Springer, 1998.
- [22] S. Jia and B. Mao, “Research on CFCM: Car Following Model Using Cloud Model Theory,” J. of Transportation Systems Engineering and Information Technology, Vol.7, Issue 6, pp. 67-73, 2007.
- [23] Q. Zhang and M. Zhong, “Using Multi-level Fuzzy Comprehensive Evaluation to Assess Reservoir Induced Seismic Risk,” J. of Computer, Vol.6, No.8, pp. 1670-1676, 2011.
- [24] H. Wang, S. He, X. Liu et al., “Simulating urban expansion using a cloud-based cellular automata model: A case study of Jiangxia, Wuhan, China,” Landscape and Urban Planning, Vol.110, pp. 99-112, 2013.
- [25] Z. Yang and D. Li, “Planar model and its application in prediction,” Chinese J. of Computers, Vol.21, No.11, pp. 961-969, 1998 (in Chinese).
- [26] D. Li, J. Han, X. Shi, and M. C. Chan, “Knowledge representation and discovery based on linguistic atoms,” Knowledge-Based Systems, Vol.10, Issue 7, pp. 431-440, 1998.
- [27] X.-F. Cheng, D.-D. Han, P. Han et al., “Flood Loss Assessment in Chaohu Basin Based on Grid Data,” Resources and Environment in the Yangtze Basin, Vol.23, No.10, pp. 1479-1484, 2014 (in Chinese).
- [28] C. Zhou, Q. Wan, S. Huang, and D. Chen, “A GIS-based Approach to Flood Risk Zonation,” Acta Geographica Sinica, Vol.67, No.1, pp. 15-24, 2000.
- [29] Y. Jie, Z. Pei, X. Chen et al., “GIS-based flood disaster risk assessment in Wuling Mountain Region,” Trans. of the Chinese Society of Agricultural Engineering, Vol.29, No.24, pp. 110-117, 2013 (in Chinese).
- [30] G. Wang, C. Xu, and D. Li, “Generic normal cloud model,” Information Sciences, Vol.280, pp. 1-15, 2014.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.