Eatesam et al. (2026) Quantifying the attribution of ecohydrological degradation: a comparative deep learning approach in a changing environment

Identification

• Journal: Journal of Hydrology
• Year: 2026
• Date: 2026-03-01
• Authors: Ali Akbar Eatesam, Khosrow Hoseini, Hojat Karami
• DOI: 10.1016/j.jhydrol.2026.135260

Research Groups

[Information not available in the provided text.]

Short Summary

This paper aims to quantify the attribution of ecohydrological degradation in a changing environment using a comparative deep learning approach.

Objective

• To quantify the attribution of ecohydrological degradation in a changing environment.

Study Configuration

• Spatial Scale: [Information not available in the provided text.]
• Temporal Scale: [Information not available in the provided text.]

Methodology and Data

• Models used: Comparative deep learning approach.
• Data sources: [Information not available in the provided text.]

Main Results

• [Information not available in the provided text.]

Contributions

• Introduces a comparative deep learning approach for attributing ecohydrological degradation, potentially offering new insights into environmental changes.

Funding

• [Information not available in the provided text.]

Citation

@article{Eatesam2026Quantifying,
  author = {Eatesam, Ali Akbar and Hoseini, Khosrow and Karami, Hojat},
  title = {Quantifying the attribution of ecohydrological degradation: a comparative deep learning approach in a changing environment},
  journal = {Journal of Hydrology},
  year = {2026},
  doi = {10.1016/j.jhydrol.2026.135260},
  url = {https://doi.org/10.1016/j.jhydrol.2026.135260}
}