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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/60460709:41330/25:105970

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10509 - Meteorology and atmospheric sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Journal of Hydrometeorology

ISSN

1525-755X

e-ISSN

1525-7541

Svazek periodika

26

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

19

Strana od-do

1775-1793

Kód UT WoS článku

001611804200003

EID výsledku v databázi Scopus

2-s2.0-105023385400