Drought characteristics prediction using a hybrid machine learning model with correction DOI

Ruihua Xue,

Jungang Luo,

Shaoxuan Li

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 23, 2024

Language: Английский

A novel implementation of pre-processing approaches and hybrid kernel-based model for short- and long-term groundwater drought forecasting DOI
Saman Shahnazi, Kiyoumars Roushangar, Seyed Hossein Hashemi

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: 652, P. 132667 - 132667

Published: Jan. 6, 2025

Language: Английский

Citations

3
Multi-Step Forecasting of Meteorological Time Series Using CNN-LSTM with Decomposition Methods DOI
Eluã Ramos Coutinho, Jonni Guiller Ferreira Madeira, Dérick G. F. Borges

et al.

Water Resources Management, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 15, 2025

Language: Английский

Citations

2
Improving Drought Prediction Accuracy: A Hybrid EEMD and Support Vector Machine Approach with Standardized Precipitation Index DOI
Reza Rezaiy, Ani Shabri

Water Resources Management, Journal Year: 2024, Volume and Issue: 38(13), P. 5255 - 5277

Published: June 29, 2024

Language: Английский

Citations

7
Characterizing drought prediction with deep learning: A literature review DOI Creative Commons
Aldo Márquez-Grajales,

Ramiro Villegas-Vega,

Fernando Salas-Martínez

et al.

MethodsX, Journal Year: 2024, Volume and Issue: 13, P. 102800 - 102800

Published: June 13, 2024

Drought prediction is a complex phenomenon that impacts human activities and the environment. For this reason, predicting its behavior crucial to mitigating such effects. Deep learning techniques are emerging as powerful tool for task. The main goal of work review state-of-the-art characterizing deep used in drought results suggest most widely climate indexes were Standardized Precipitation Index (SPI) Evapotranspiration (SPEI). Regarding multispectral index, Normalized Difference Vegetation (NDVI) indicator utilized. On other hand, countries with higher production scientific knowledge area located Asia Oceania; meanwhile, America Africa regions few publications. Concerning methods, Long-Short Term Memory network (LSTM) algorithm implemented task, either canonically or together (hybrid methods). In conclusion, reveals need more about using indices Africa; therefore, it an opportunity characterize developing countries.

Language: Английский

Citations

5
An innovative hybrid W-EEMD-ARIMA model for drought forecasting using the standardized precipitation index DOI
Reza Rezaiy, Ani Shabri

Natural Hazards, Journal Year: 2024, Volume and Issue: unknown

Published: July 3, 2024

Language: Английский

Citations

4
Modeling the effect of meteorological drought on lake level changes with machine learning techniques DOI
Özlem Terzi, Dilek Taylan, Tahsin Baykal

et al.

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 227 - 246

Published: Jan. 1, 2025

Language: Английский

Citations

0
Interpretability of compound drought-hot extreme index prediction model: a regional study in Iran DOI
Mahnoosh Moghaddasi,

Kimia Naderi,

Mansour Moradi

et al.

Environmental Science and Pollution Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 17, 2025

Language: Английский

Citations

0
A Variational Mode Decomposition–Grey Wolf Optimizer–Gated Recurrent Unit Model for Forecasting Water Quality Parameters DOI Creative Commons
Binglin Li,

Fengyu Sun,

Yufeng Lian

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(14), P. 6111 - 6111

Published: July 13, 2024

Water is a critical resource globally, covering approximately 71% of the Earth’s surface. Employing analytical models to forecast water quality parameters based on historical data key strategy in field monitoring and treatment. By using forecasting model, potential changes can be understood over time. In this study, gated recurrent unit (GRU) neural network was utilized dissolved oxygen levels following variational mode decomposition (VMD). The GRU network’s were optimized grey wolf optimizer (GWO), leading development VMD–GWO–GRU model for parameters. results indicate that outperforms both standalone GWO–GRU capturing information related Additionally, it shows improved accuracy medium long-term changes, resulting reduced root mean square error (RMSE) absolute percentage (MAPE). demonstrates significant improvement lag parameters, ultimately boosting accuracy. This approach applied effectively serving as solid foundation future treatment strategies.

Language: Английский

Citations

1
A Novel Method Based on Stepwise Variational Modal Decomposition and Gramian Angular Difference Field for Bearing Health Monitoring DOI
Yong Li,

Hongyao Zhang,

Sencai Ma

et al.

Arabian Journal for Science and Engineering, Journal Year: 2024, Volume and Issue: 49(11), P. 15773 - 15786

Published: Aug. 5, 2024

Language: Английский

Citations

1
Drought characteristics prediction using a hybrid machine learning model with correction DOI

Ruihua Xue,

Jungang Luo,

Shaoxuan Li

et al.

Stochastic Environmental Research and Risk Assessment, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 23, 2024

Language: Английский

Citations

0