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An improved SPEI drought forecasting approach using the long short-term memory neural network.
Abhirup Dikshit1, Biswajeet Pradhan2, Alfredo Huete3
1Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney, NSW, 2007, Australia.
Journal of Environmental Management
|January 22, 2021
Summary
This study introduces Long Short-Term Memory (LSTM) for drought forecasting, showing improved accuracy in predicting drought intensity and categories compared to traditional machine learning models.
Area of Science:
- Environmental Science
- Climate Science
- Data Science
Background:
- Droughts pose significant socio-economic risks due to their slow-moving and widespread nature.
- Accurate drought forecasting models are crucial for understanding and mitigating drought impacts.
- Deep learning methods, like LSTM, offer potential for improved drought characteristic analysis but remain under-explored.
Purpose of the Study:
- To evaluate the effectiveness of a Long Short-Term Memory (LSTM) deep learning model for predicting drought using the Standard Precipitation Evaporation Index (SPEI).
- To compare the LSTM model's performance against traditional machine learning methods (Random Forests, Artificial Neural Networks) for drought forecasting.
- To analyze the model's capability in predicting drought intensity, category, and spatial variation.
Main Methods:
- Utilized Long Short-Term Memory (LSTM) deep learning model for predicting SPEI at 1- and 3-month time scales.
- Employed hydro-meteorological variables from the Climatic Research Unit (CRU) dataset (1901-2018) as predictors.
- Compared LSTM performance against Random Forests and Artificial Neural Networks using statistical metrics (R², RMSE, MAE) and ROC-AUC for drought category analysis.
Main Results:
- LSTM achieved high accuracy in predicting drought intensity, with R² values exceeding 0.99 for both SPEI 1 and SPEI 3.
- The model demonstrated strong performance in forecasting drought categories, achieving ROC-AUC values of 0.83 for SPEI 1 and 0.82 for SPEI 3.
- Analysis of spatial variations showed improved drought forecasting for a 1-month lead time compared to other machine learning models.
Conclusions:
- The LSTM model shows significant promise for enhancing drought forecasting accuracy and understanding drought characteristics.
- This deep learning approach offers a valuable tool for drought mitigation strategies.
- Further research should explore diverse models to continually improve drought prediction capabilities.
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