Earth Sciences

Special Issue

Predicting Hydrological Drought Using Deep Learning Models

Submission deadline: Aug. 01, 2024
Status: Open for Submission
Special Issue of Earth Sciences
Climate change negatively affects water resources, dams, and water quality worldwide. Therefore, water and sewerage administrations, experts, and academics have focused on predicting variables such as precipitation, temperature, and evaporation, or developing future scenarios. One of the most popular prediction approaches applied in recent years is deep learning methods. One of the crucial aspects that needs to be emphasized here is the detailed classification process. Drought indices such as SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation-Evapotranspiration Index) are commonly used for classification purposes. Comparing the prediction accuracies obtained by new classification water levels of a dam that has experienced a real drought event over a long-term period, new categorizing dam storage rates, and comparing these accuracy results with SPI, SPEI, and similar approaches can be beneficial in improving the prediction accuracy. Indeed, in the context of deep learning approaches, directing researchers towards classification outputs rather than numerical outputs can be more important. By focusing on classification outputs, researchers can obtain valuable insights into the categorization and identification of different patterns and classes within the data, which can be useful for decision-making and understanding the severity of drought conditions. With the development of these new classification methods, hydraulic droughts can be predicted more realistically. By utilizing advanced deep learning methods and incorporating relevant features and variables, these classification methods can provide more accurate and reliable predictions for hydraulic droughts. This can help administrations, as well as other stakeholders, in taking proactive measures to mitigate the impacts of droughts and effectively manage water resources.


  1. Deep Learning Methods
  2. Long Short-Term Memory Networks (LTSM)
  3. Convolutional Neural Networks (CNN)
  4. Drought Risk Assessment
  5. Water Scarcity
  6. Climate Adaptation Strategies
  7. Hydraulic Drought
  8. Standardized Precipitation Index (SPI)
  9. Standardized Precipitation and Evapotranspiration Index (SPEI)
  10. Drought Monitoring
Lead Guest Editor
Guest Editor
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