JDR Vol.12 No.1 pp. 187-197
doi: 10.20965/jdr.2017.p0187


Agrometeorological Disaster Grading in Guangdong Province Based on Data Mining

Danni Wang*1, Shitai Bao*2,†, Chunlin Wang*3, and Chongyang Wang*4

*1Department of Resources and the Urban Planning, XinHua College of Sun Yat-sen University
721 Guang Shan 1st Road, Tian He District, Guangzhou 510520, China

*2The College of Natural Resources and Environment, South China Agriculture University
483 Wushan Road, TianHe District, Guangzhou 510642, China

*3Climate Center of Guangdong Province
68 Gongyeyilu Road, Zhicun, Dashi Street, Panyu District, Guangzhouv 511430, China

*4Guangzhou Institute of Geochemistry, Chinese Academy of Sciences
100 Xianlie Road, Yuexiu District, Guangzhou 510070, China

Corresponding author

September 9, 2016
January 12, 2017
February 1, 2017
fuzzy associate rule, data mining, agrometeorological disaster monitoring and early warning
This study proposes a mining method for meteorological disaster grade rules from the raw data accumulated by meteorological stations using fuzzy association rules. Rules for grading agrometeorological disasters are created and successfully applied to a map. The intention is to mitigate such disasters by understanding their conditions. The procedure described uses the fuzzy c-means clustering algorithm and the Apriori algorithm to mine fuzzy association rules for high-temperature and flooding agrometeorological disasters in Guangdong province, China. In the proposed method, the clustering algorithm does not depend on the membership functions of domain experts. The results show that effective association rules for agrometeorological disasters can be obtained from meteorological data in the long term, even with a lack of prior knowledge. The rules obtained could be used to forecast the grade and region of such disasters in Guangdong province, thus contributing to agrometeorological disaster monitoring and early warning efforts.
Cite this article as:
D. Wang, S. Bao, C. Wang, and C. Wang, “Agrometeorological Disaster Grading in Guangdong Province Based on Data Mining,” J. Disaster Res., Vol.12 No.1, pp. 187-197, 2017.
Data files:
  1. [1] S. Ikeda, “Special Issue on Adaptation to Global-Warming-Triggered Disasters,” J. Disaster Res., Vol.4, No.1, pp. 1-2, 2009.
  2. [2] S. Kure, T. Tebakari, and M. Miyamoto, “Review of Recent Water-Related Disasters and Scientific Activities in Southeast Asia: Lessons Learned and Future Challenges for Disaster Risk Reduction,” J. Disaster Res.. Vol.11, No.3, pp. 394-401, 2016.
  3. [3] P. K. Rawat, P. C. Tiwari, and C. C. Pant, “Geo-hydrological database modeling for integrated multiple hazards and risk assessment in Lesser Himalaya: a GIS-based case study,” Natural Hazards, Vol.62, No.3, pp. 1233-1260, 2012.
  4. [4] W. Thavorntam, N. Tantemsapya, and L. Armstrong, “A combination of meteorological and satellite-based drought indices in a better drought assessment and forecasting in Northeast Thailand,” Natural Hazards, Vol.77, No.3, pp. 1453-1474, 2015.
  5. [5] Z.-G. Li, “Influence of agricultural meteorological disasters on grain production in Henan province,” J. of ARID Land Resources and Environment, Vol.5, pp. 126-130, 2013.
  6. [6] J. Shi and L.-L. Cui, “Characteristics of high impact weather and meteorological disaster in Shanghai, China,” Natural Hazards, Vol.60, No.3, pp. 951-969, 2012.
  7. [7] X.-N. Guo, “Analysis on Guangdong agriculture climatic resource characteristic and the main meteorological disaster,” Guangdong Agricultural Sciences, Vol.6, pp. 181-185+2, 2013.
  8. [8] Y.-J. Li, C.-Y. Wang, B. Zhao, et al., “Effects of climate change on agricultural meteorological disaster and crop insects diseases,” Trans. of the Chinese Society of Agricultural Engineering, Vol.S1, pp. 263-271, 2010.
  9. [9] W.-P. Lou, X.-M. Zhu, S.-Q. Zhou, et al., “Monitoring and early war ning system for agro-ecological and agricultural Meteorological disaster in Shaoxing City,” Trans. of the Chinese Society of Agricultural Engineering, Vol.12, pp. 182-186+3, 2007.
  10. [10] J.-F. Mo, S.-Q. Zhong, Y.-L. Chen, et al., “Development and application of monitoring and early warning system for main agro-meteorological disasters in Guangxi Province,” J. of Natural Disasters, Vol.2, pp. 150-157, 2013.
  11. [11] H. Oshikawa, A. Hashimoto, K. Tsukahara et al., “Impacts of Recent Climate Change on Flood Disaster and Preventive Measures,” J. Disaster Res., Vol.3, No.2, pp. 131-141, 2008.
  12. [12] N. Shuto, S. Ikeda, and S. Egashira, “Special Issue on Meteorological Disasters and Water Disasters in Urban Areas,” J. Disaster Res., Vol.2, No.3, pp. 133, 2007.
  13. [13] J. F. Peters, Z. Suraj, S. Shan, et al., “Classification of meteorological volumetric radar data using rough set methods,” Pattern Recognition Letters, Vol.24, No.6, pp. 911-920, 2003.
  14. [14] J. Bartok, O. Habala, P. Bednar, et al., “Data mining and integration for predicting significant meteorological phenomena,” Procedia Computer Science, Vol.1, pp. 37-46, 2012.
  15. [15] M.-Y. Li, X.-T. Fang, and W.-Q. Jiang, “Mining of spatial data of forest resources based on GIS-A case study of zijin mountain,” J. of Northwest Forestry University, Vol.27, No.3, pp. 180-186, 2012.
  16. [16] E. Guo, J. Zhang, X. Ren, Q. Zhang, and Z. Sun, “Integrated risk assessment of flood disaster based on improved set pair analysis and the variable fuzzy set theory in central Liaoning Province, China,” Natural Hazards, Vol.74, No.2, pp. 947-965, 2014.
  17. [17] T.-F. Zhu, D.-M. Zhu, and Y.-L. Ding, “Application of relation model in the disater weather,” Computer Science, Vol.38, No.7, pp. 108-110, 2011.
  18. [18] W.-N. Shu and L.-X. Ding, “ECOGA: Efficient Data Mining Approach for Fuzzy Association Rules,” J. of Software, Vol.6, No.1, pp. 91-99, 2011.
  19. [19] L. Wang, L. Feng, and M.-F. Wu, “AT-Mine: An Efficient Algorithm of Frequent Itemset Mining on Uncertain Dataset,” J. of Computers, Vol.8, No.6, pp. 1417-1426, 2013.
  20. [20] J. Wu and X.-M. Li, “An Effective Mining Algorithm for Weighted Association Rules in Communication Networks,” J. of Computers, Vol.3, No.10, pp. 20-27, 2008.
  21. [21] N.-N. Li, J. Song, J.-H. Gu, et al., “Research on mining association rules of disaster weather in weather database,” J. of Hebei University of Technology, Vol.2, pp. 68-73, 2005.
  22. [22] D. Mahashweta and P. Srinivasan, “Anomaly Detection and Spatio-Temporal Analysis of Global Climate System,” Knowledge Discovery from Sensor Data, pp. 142-150, 2009.
  23. [23] O. Peter, M. Natasa, M.-L. Freidrich, et al., “A Data Mining Approach for Capacity Building of Stakeholders in Integrated Flood Management,” 6th IEEE Int. Conf. on Data Mining, pp. 446-455, 2006.
  24. [24] Z.-J. Lv, Z.-F. Wang, F.-D. Xie, et al., “Fuzzy association rules mining from time series based on FCM clustering,” J. of Dalian University of Technology (Social Sciences), Vol.5, pp. 806-810, 2010.
  25. [25] Z.-L. Wu, F.-G. Xiong, and M.-G. Teng, “Mining fuzzy association rules for numerical attributes based on fuzzy clustering,” Mini-micro Systems, Vol.7, pp. 1295-1297, 2004.
  26. [26] H.-Y. Zhao, L.-C. Cai, and X.-J. Li, “Overview of association rules apriori mining algorithm,” J. of Sichuan University of Science & Engineering (Natural Science Edition), Vol.1, pp. 66-70, 2011.
  27. [27] T.-Q. Zhu and P. Xiong, “An Algorithm of Mining Fuzzy Associate Rules,” J. of Wuhan Polytechnic University, Vol.1, pp. 24-28, 2005.
  28. [28] H. Wang, X.-G. Chen, F. Hu, et al., “Adaptative adjustments of the sowing date of late season rice under climate change in Guangdong Province,” ACTA Ecologica Sinica, Vol.15, pp. 4261-4269, 2011.
  29. [29] Y.-F. Ren, P. Gao, and C.-Y. Wang, “High temperature damage to paddy rice in Jiangsu Province and its cause analysis,” J. of Natural Disasters, Vol.5, pp. 101-107, 2010.
  30. [30] C.-R. Peng, X.-L. Liu, M.-D. Li, et al., “Main Weather Disasters of Rice and Defensive Counter measures in Jiangxi,” ACTA Agriculture Jiangxi, Vol.4, pp. 127-130, 2005.
  31. [31] Q. Zhang, Y.-X. Zhao, and C.-Y. Wang, “Advances in research on major agro-meteorological disaster indexe in China,” J. of Natural Disasters, Vol.6, pp. 40-54, 2010.
  32. [32] H.-M. Tang, X.-J. Shuai, X.-P. Xiao, et al., “Analysis and countermeasures on the Agro-meteorological disasters of Hunan province in 2010,” Chinese Agricultural Science Bulletin, Vol.12, pp. 284-290, 2012.
  33. [33] A. Hasegawa, M. Gusyev, and Y. Iwami, “Meteorological Drought and Flood Assessment Using the Comparative SPI Approach in Asia Under Climate Change,” J. Disaster Res., Vol.11, No.6, pp. 1082-1090, 2016.
  34. [34] Y. Hisada, Y. Sugihara, and N. Matsunaga, “Meteorological Characteristics of Local Heavy Rainfall in the Fukuoka Plain,” J. Disaster Res., Vol.10, No.3, pp. 429-435, 2015.

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