Developing a novel hybrid model based on GRU deep neural network and Whale optimization algorithm for precise forecasting of river’s streamflow DOI Creative Commons
Amin Gharehbaghi, Redvan Ghasemlounıa, Farshad Ahmadi

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Июнь 3, 2025

Streamflow contemplates a fundamental criterion to evaluate the impact of human activities and climate changes on hydrological cycle. In this study, novel innovative deep neural network (DNN) structure by integrating double Gated Recurrent Units (GRU) model with multiplication layer meta-heuristic whale optimization algorithm (WOA) (i.e., hybrid 2GRU×-WOA model) is developed improve prediction accuracy performance mean monthly Chehel-Chai River's streamflow (CCRSFm) in Iran. The Pearson's correlation coefficient (PCC) Cosine Amplitude Sensitivity (CAS) as feature (input) selection process determine only precipitation (Pm) most effective input variable among list on-site potential time series parameters recorded study area. Thanks well-proportioned structural framework suggested model, it leads an appropriate total learnable parameter (TLP) compared standard individual GRU Bi-GRU benchmark models comparable meta-parameters. This under optimal meant meta-parameters tuned i.e., coupling state activation functions (SAF) tanh-softsign, dropout rate (P-rate) 0.5, numbers hidden neurons (NHN) 70, outperforms R2 0.79, NSE 0.76, MAE 0.21 (m3/s), MBE -0.11(m3/s), RMSE 0.36 (m3/s). Hybridizing 2GRU× WOA causes increase value 6.8% reduce 20.4%. Comparatively, result 0.59 0.66, 0.55 0.6, 0.91 0.53 0.047 - 0.06 1.29 0.83 respectively.

Язык: Английский

Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets DOI Open Access
F. M. Hasan,

Paul Medley,

Jason Drake

и другие.

Water, Год журнала: 2024, Номер 16(13), С. 1904 - 1904

Опубликована: Июль 3, 2024

Machine learning (ML) applications in hydrology are revolutionizing our understanding and prediction of hydrological processes, driven by advancements artificial intelligence the availability large, high-quality datasets. This review explores current state ML hydrology, emphasizing utilization extensive datasets such as CAMELS, Caravan, GRDC, CHIRPS, NLDAS, GLDAS, PERSIANN, GRACE. These provide critical data for modeling various parameters, including streamflow, precipitation, groundwater levels, flood frequency, particularly data-scarce regions. We discuss type methods used significant successes achieved through those models, highlighting their enhanced predictive accuracy integration diverse sources. The also addresses challenges inherent applications, heterogeneity, spatial temporal inconsistencies, issues regarding downscaling LSH, need incorporating human activities. In addition to discussing limitations, this article highlights benefits utilizing high-resolution compared traditional ones. Additionally, we examine emerging trends future directions, real-time quantification uncertainties improve model reliability. place a strong emphasis on citizen science IoT collection hydrology. By synthesizing latest research, paper aims guide efforts leveraging large techniques advance enhance water resource management practices.

Язык: Английский

Процитировано

11
Evaluation of Solar Radiation Prediction Models Using AI: A Performance Comparison in the High-Potential Region of Konya, Türkiye DOI Creative Commons
Vahdettin Demir

Atmosphere, Год журнала: 2025, Номер 16(4), С. 398 - 398

Опубликована: Март 30, 2025

Solar radiation is one of the most abundant energy sources in world and a crucial parameter that must be researched developed for sustainable projects future generations. This study evaluates performance different machine learning methods solar prediction Konya, Turkey, region with high potential. The analysis based on hydro-meteorological data collected from NASA/POWER, covering period 1 January 1984 to 31 December 2022. compares Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), GRU (Bi-GRU), LSBoost, XGBoost, Bagging, Random Forest (RF), General Regression Neural Network (GRNN), Support Vector Machines (SVM), Artificial Networks (MLANN, RBANN). variables used include temperature, relative humidity, precipitation, wind speed, while target variable radiation. dataset was divided into 75% training 25% testing. Performance evaluations were conducted using Mean Absolute Error (MAE), Root Square (RMSE), coefficient determination (R2). results indicate Bi-LSTM models performed best test phase, demonstrating superiority deep learning-based approaches prediction.

Язык: Английский

Процитировано

1
Assessment of hybrid kernel function in extreme support vector regression model for streamflow time series forecasting based on a bayesian estimator decomposition algorithm DOI
Peng Shi, Lei Xu, Simin Qu

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2025, Номер 149, С. 110514 - 110514

Опубликована: Март 15, 2025

Язык: Английский

Процитировано

1
Developing a novel hybrid model based on GRU deep neural network and Whale optimization algorithm for precise forecasting of river’s streamflow DOI Creative Commons
Amin Gharehbaghi, Redvan Ghasemlounıa, Farshad Ahmadi

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Июнь 3, 2025

Streamflow contemplates a fundamental criterion to evaluate the impact of human activities and climate changes on hydrological cycle. In this study, novel innovative deep neural network (DNN) structure by integrating double Gated Recurrent Units (GRU) model with multiplication layer meta-heuristic whale optimization algorithm (WOA) (i.e., hybrid 2GRU×-WOA model) is developed improve prediction accuracy performance mean monthly Chehel-Chai River's streamflow (CCRSFm) in Iran. The Pearson's correlation coefficient (PCC) Cosine Amplitude Sensitivity (CAS) as feature (input) selection process determine only precipitation (Pm) most effective input variable among list on-site potential time series parameters recorded study area. Thanks well-proportioned structural framework suggested model, it leads an appropriate total learnable parameter (TLP) compared standard individual GRU Bi-GRU benchmark models comparable meta-parameters. This under optimal meant meta-parameters tuned i.e., coupling state activation functions (SAF) tanh-softsign, dropout rate (P-rate) 0.5, numbers hidden neurons (NHN) 70, outperforms R2 0.79, NSE 0.76, MAE 0.21 (m3/s), MBE -0.11(m3/s), RMSE 0.36 (m3/s). Hybridizing 2GRU× WOA causes increase value 6.8% reduce 20.4%. Comparatively, result 0.59 0.66, 0.55 0.6, 0.91 0.53 0.047 - 0.06 1.29 0.83 respectively.

Язык: Английский

Процитировано

0