Combination of Hybrid Artificial Neural Networks with Particle Swarm Optimization Algorithm for SPEI Forecasting

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388998%3A_____%2F25%3A00642130" target="_blank" >RIV/61388998:_____/25:00642130 - isvavai.cz</a>

Alternative codes found

RIV/60460709:41330/25:105970

Result on the web

<a href="https://journals.ametsoc.org/view/journals/hydr/26/11/JHM-D-25-0034.1.xml" target="_blank" >https://journals.ametsoc.org/view/journals/hydr/26/11/JHM-D-25-0034.1.xml</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1175/JHM-D-25-0034.1" target="_blank" >10.1175/JHM-D-25-0034.1</a>

Result language

angličtina

Original language name

Combination of Hybrid Artificial Neural Networks with Particle Swarm Optimization Algorithm for SPEI Forecasting

Original language description

Forecasting drought is critical to mitigate its potential impacts on agriculture, water resources, and ecosystems. In this study, we focused on predicting the standardized precipitation evapotranspiration index (SPEI), a widely used climatic water balance indicator. We developed a hybrid modeling framework that combines artificial neural networks (ANNs) with particle swarm optimization (PSO) to train network weights. The study evaluated the influence of four factors on SPEI forecasting performance: the PSO variant, the number of input variables, the number of hidden-layer neurons, and the choice of objective function. A total of 150 model configurations were tested using long-term meteorological data from eight U.S. catchments from the Model Parameter Estimation Experiment (MOPEX) database. Results showed that the APartPSO variant achieved the best optimization performance, and the Nash-Sutcliffe efficiency was the most effective objective function. These findings confirm that the integration of ANN with PSO is suitable for forecasting the SPEI and suggest that advanced PSO variants can be effectively applied to other inverse modeling problems in hydrology. SIGNIFICANCE STATEMENT: Drought prediction is essential for effective water resource management and minimizing environmental and societal impacts. This study presents a new approach for forecasting the standardized precipitation evapotranspiration index by combining artificial neural networks (ANNs) with particle swarm optimization (PSO). Applied to eight U.S. catchments, the hybrid ANN-PSO model performed best when using the APartPSO variant for training and the Nash-Sutcliffe efficiency as the objective function. These results support the use of hybrid PSO-ANN models in drought monitoring and planning. The approach offers a flexible framework that can be adapted to other regions. Future research could incorporate additional climate variables to further enhance prediction accuracy.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10509 - Meteorology and atmospheric sciences

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Hydrometeorology

ISSN

1525-755X

e-ISSN

1525-7541

Volume of the periodical

26

Issue of the periodical within the volume

11

Country of publishing house

US - UNITED STATES

Number of pages

19

Pages from-to

1775-1793

UT code for WoS article

001611804200003

EID of the result in the Scopus database

2-s2.0-105023385400