Deep learning models for drought susceptibility mapping in Southeast Queensland, Australia
Article
Rezaie, Fatemeh, Panahi, Mahdi P, Jun, Changhyun, Dayal, Kavina, Kim, Dongkyun, Darabi, Hamid, Kalantari, Zahra, Seifollahi-Aghmiuni, Samaneh, Deo, Ravinesh C. and Bateni, Sayed M.. 2025. "Deep learning models for drought susceptibility mapping in Southeast Queensland, Australia." Stochastic Environmental Research and Risk Assessment. https://doi.org/10.1007/s00477-024-02879-w
| Article Title | Deep learning models for drought susceptibility mapping in Southeast Queensland, Australia |
|---|---|
| ERA Journal ID | 864 |
| Article Category | Article |
| Authors | Rezaie, Fatemeh, Panahi, Mahdi P, Jun, Changhyun, Dayal, Kavina, Kim, Dongkyun, Darabi, Hamid, Kalantari, Zahra, Seifollahi-Aghmiuni, Samaneh, Deo, Ravinesh C. and Bateni, Sayed M. |
| Journal Title | Stochastic Environmental Research and Risk Assessment |
| Number of Pages | 17 |
| Year | 2025 |
| Publisher | Springer |
| ISSN | 1436-3240 |
| 1436-3259 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/s00477-024-02879-w |
| Web Address (URL) | https://link.springer.com/article/10.1007/s00477-024-02879-w |
| Abstract | Drought is a global phenomenon with significant negative impacts on water availability, agricultural production, livelihoods, and socioeconomic conditions. Despite its destructive effects, spatially predicting drought hazards remains a challenging task. This study developed an innovative framework by leveraging two state-of-the-art deep learning models: convolutional neural networks (CNNs) and the long short-term memory (LSTM) model. Key predictive factors, including the topographic wetness index, soil depth, mean annual precipitation, elevation, slope, sand content, clay content, and plant-available water-holding capacity (PAWC), were carefully selected for analysis. An agricultural drought inventory map was generated based on the relative departure of soil moisture. The performance of the CNN and LSTM models was evaluated using root mean square error (RMSE), standard deviation (StD), and the area under the receiver operating characteristic curve (AUC). The results indicated that certain parts of the research area were highly susceptible to drought. Both models performed well, achieving AUC values of 81.9% (CNN) and 81.7% (LSTM). The RMSE and StD further confirmed the predictive capabilities of these models. Sensitivity analyses highlighted the importance of PAWC, mean annual precipitation, and clay fraction in detecting drought-prone areas. The drought susceptibility map provides valuable insights into the vulnerability and likelihood of an area experiencing drought conditions, offering essential information for decision-makers to effectively prioritize resources and mitigate drought impacts. |
| Keywords | Convolutional neural network; Food security; Soil moisture; Drought; Deep learning |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 461103. Deep learning |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Korea Institute of Geoscience and Mineral Resources (KIGAM), Korea |
| Korea University of Science and Technology, Korea | |
| Stockholm University, Sweden | |
| Korea University, Seoul, Korea | |
| Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia | |
| Hongik University, Korea | |
| University of Helsinki, Finland | |
| KTH Royal Institute of Technology, Sweden | |
| School of Mathematics, Physics and Computing | |
| University of Hawaii, United States | |
| University of South Africa, Muckleneuk Ridge, South Africa |
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