Abstract
Due to the multidimensional nature of drought and the limitations of single-variable indices in capturing combined meteorological, hydrological, and socioeconomicimpacts, predicting hybrid droughts is valuable insights for water resource management. This study proposes an Index-fusion-based framework for hybrid drought modeling in the Marun basin, Iran, during the historical (2002–2021) and future (2022–2040) periods under climate change scenarios (SSP3.70 and SSP5.85). Multiple standard drought indices based on precipitation, inflow, reservoir storage, and discharge data are combined using Shannon entropy (SE), Copula functions (C), and Principal Component Analysis (PCA), respectively, to propose three hybrid drought indices including Shannon Entropy-Hybrid Drought Index (SEHDI), Copula-Hybrid Drought Index (CHDI), and PCA-Hybrid drought index (PCAHDI). After evaluating optimal time scales of univariate indices, the CHDI is chosen for its comprehensive and smoothed representation of drought evolution and better reflect of drought duration and severity. Then, three Individual Artificial Intelligence (IAI) models, including Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), and Random Forest (RF), are used, and the results are applied to predict the HDIs. Here, the MLP and RF models are also used to fuse the IAI modeling to improve predictive accuracy. The results show that, the MLP fusion-based model performswell for CHDI under SSP3.70, and RF fusion-based model performswell for CHDI under SSP5.85. When fusion-based models are used, based on the error metrics, the accuracy of HDI index estimations is significantly improved to 2.4% under SSP3.70. The results suggest an increased risk of drought under the high-emission SSP5.85 scenario, but a decreased risk of drought under the more moderate SSP3.70 scenario, highlighting the usefulness of the proposed framework for drought monitoring and water management planning.
Similar content being viewed by others
Comparative multi-index analysis of existing drought typology and environmental droughts in a climate-stressed region
Assessing trends and forecasting meteorological drought in South Africa using Savitzky–Golay enhanced hybrid deep learning
Examination of hydrological variations and their effect on water shortage trends and water-energy production using convolutional neural network and ISSA
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sector.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethical approval
This article does not contain any studies with human participants or animals performedby any of the authors.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissions
About this article
Cite this article
Klaho, M.H., Moeini, R. & Alijanian, M. A hybrid drought index prediction framework under climate change scenarios by equipping index fusion with an individual artificial intelligence method.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-71545-8
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-71545-8
Keywords
- Hybrid drought index
- Individual Artificial Intelligence
- Shannon entropy
- Copula functions
- Fusion-Based
- Principal Component Analysis
- Climate change
Source: Resources - nature.com
