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Drought is one of the most frequently occurring natural disasters and it has significant effects on surface and groundwater availability and quality, agriculture and ecosystems. Drought indices, which are employed to quantify the severity of drought by measuring and comparing hydro-climatic factors, are key tools for making decisions on water resources management to mitigate the impact of drought and assist successful drought monitoring and assessment. In this study, the capability of Fuzzy Logic (FL)- based drought indices was studied and compared with the widely used conventional drought indices (i.e., SPI, RAI and SPEI) in Goulburn Basin, Australia. The results indicated the superiority of FL-based indices over the studied conventional indices. FL-based index with rainfall, maximum temperature and PET input showed the highest performance, however, simple FL index with only rainfall and mean temperature input performed almost as well as more complex FL indices. RAI was the best-performing index among the studied conventional indices. The results of this study demonstrate the capability of the FL model in effective drought management.

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Fuzzy Logic-Based Drought Index: A Case Study of Goulburn Basin in Victoria, Australia

  • 会议论文

  • 发表日期：

    2026年

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  • Abdullah Gokhan Yilmaz,
  • Serter Atabay,
  • Monzur Imteaz,
  • Mhamd Saifaldeen Oyounalsoud,
  • Mohamed Abdallah

摘要

Drought is one of the most frequently occurring natural disasters and it has significant effects on surface and groundwater availability and quality, agriculture and ecosystems. Drought indices, which are employed to quantify the severity of drought by measuring and comparing hydro-climatic factors, are key tools for making decisions on water resources management to mitigate the impact of drought and assist successful drought monitoring and assessment. In this study, the capability of Fuzzy Logic (FL)- based drought indices was studied and compared with the widely used conventional drought indices (i.e., SPI, RAI and SPEI) in Goulburn Basin, Australia. The results indicated the superiority of FL-based indices over the studied conventional indices. FL-based index with rainfall, maximum temperature and PET input showed the highest performance, however, simple FL index with only rainfall and mean temperature input performed almost as well as more complex FL indices. RAI was the best-performing index among the studied conventional indices. The results of this study demonstrate the capability of the FL model in effective drought management.