Chehreh et al. (2025) An impact-oriented framework for a deep learning–based composite drought index considering potential economic losses

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Environmental Research Letters
• Year: 2025
• Date: 2025-12-01
• Authors: Mostafa Khosh Chehreh, Carlo De Michele
• DOI: 10.1088/1748-9326/ae269a

Research Groups

Not explicitly stated in the provided abstract.

Short Summary

This study proposes a novel deep learning-derived drought index, shifting from traditional comparative validation to an impact-oriented evaluation using drought-induced economic losses as the primary performance metric, and offers a framework for index selection based on predictive reliability and data effort.

Objective

• To propose and evaluate a novel deep learning-derived drought index using an impact-oriented lens, specifically assessing its capacity to estimate drought-induced economic losses.

Study Configuration

• Spatial Scale: Italy
• Temporal Scale: 1989–2024

Methodology and Data

• Models used: Deep learning techniques, including convolutional neural networks, artificial neural networks, and variational autoencoders, employing different self-supervised learning architectures.
• Data sources: ERA5 reanalysis data, EM-DAT database (economic loss records).

Main Results

• A novel deep learning-derived drought index was developed and evaluated based on its ability to estimate potential economic losses caused by drought.
• The proposed framework enables users to select the most suitable drought index by balancing data collection effort and predictive reliability.

Contributions

• Proposes a novel drought index derived from deep learning techniques.
• Introduces a paradigm shift from comparative validation of drought indices to an impact-oriented evaluation using real-world economic losses.
• Develops a framework that guides users in selecting the most appropriate drought index based on a balance between data collection effort and predictive reliability.

Funding

Not explicitly stated in the provided abstract.

Citation

@article{Chehreh2025impactoriented,
  author = {Chehreh, Mostafa Khosh and Michele, Carlo De},
  title = {An impact-oriented framework for a deep learning–based composite drought index considering potential economic losses},
  journal = {Environmental Research Letters},
  year = {2025},
  doi = {10.1088/1748-9326/ae269a},
  url = {https://doi.org/10.1088/1748-9326/ae269a}
}