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Experiments to automatically monitor drought variation using simulated annealing algorithm

Author

Listed:
  • Hongbo Zhang

    (Nanjing University of Information Science and Technology
    Nanjing University of Information Science and Technology
    Nanjing University of Information Science and Technology)

  • Nan Li

    (Institute of Heavy Rain, CMA
    Nanjing University of Information Science and Technology)

  • Wengang Zhang

    (Institute of Heavy Rain, CMA)

  • Xiaofang Pei

    (Nanjing University of Information Science and Technology
    Nanjing University of Information Science and Technology)

Abstract

A drought is a period of a lack of precipitation in water-deficient areas, causing shortages in their water supply, whether atmospheric, surface, or ground water. Drought with long-duration and wide-area coverage often leads to serious social and economic losses. Consequently, drought monitoring and assessment have become a critical research topic in the area. There are a number of related studies on identifying drought with different types of data, but few aim at automatic drought tracking since drought regions are time variant. In this study, an automatic drought monitoring method is proposed based on drought region tracking. Firstly, drought regions are identified with drought indexes. A simulated annealing algorithm is then used to automatically track different drought regions in successive time intervals based on the area and location of different drought regions. Preliminary results of a case experiment indicate that the simulated annealing algorithm is suitable to be used in automatic monitors and able to achieve desirable tracking results. The proposed method based on the simulated annealing algorithm is effective for automatically monitoring the variation in drought characteristics such as the spatial extent.

Suggested Citation

  • Hongbo Zhang & Nan Li & Wengang Zhang & Xiaofang Pei, 2016. "Experiments to automatically monitor drought variation using simulated annealing algorithm," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(1), pages 175-184, October.
    • Handle: RePEc:spr:nathaz:v:84:y:2016:i:1:d:10.1007_s11069-016-2414-x
    DOI: 10.1007/s11069-016-2414-x
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    References listed on IDEAS

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    1. N. Patel & Kamana Yadav, 2015. "Monitoring spatio-temporal pattern of drought stress using integrated drought index over Bundelkhand region, India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(2), pages 663-677, June.
    2. Wei Gao, 2015. "Forecasting of rockbursts in deep underground engineering based on abstraction ant colony clustering algorithm," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 76(3), pages 1625-1649, April.
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    Cited by:

    1. Wenjuan Sun & Paolo Bocchini & Brian D. Davison, 2020. "Applications of artificial intelligence for disaster management," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(3), pages 2631-2689, September.

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