Advancing Hydrology through Machine Learning: Insights, Challenges, and Future Directions Using the CAMELS, Caravan, GRDC, CHIRPS, PERSIANN, NLDAS, GLDAS, and GRACE Datasets

Water, Journal Year: 2024, Volume and Issue: 16(13), P. 1904 - 1904

Published: July 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.

Language: Английский

Evaluation of Solar Radiation Prediction Models Using AI: A Performance Comparison in the High-Potential Region of Konya, Türkiye

Atmosphere, Journal Year: 2025, Volume and Issue: 16(4), P. 398 - 398

Published: March 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.

Language: Английский

Assessment of hybrid kernel function in extreme support vector regression model for streamflow time series forecasting based on a bayesian estimator decomposition algorithm

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 149, P. 110514 - 110514

Published: March 15, 2025

Language: Английский