Prediction of reference crop evapotranspiration based on improved convolutional neural network (CNN) and long short-term memory network (LSTM) models in Northeast China

Journal of Hydrology, Год журнала: 2024, Номер unknown, С. 132223 - 132223

Опубликована: Окт. 1, 2024

Язык: Английский

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A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning

Water, Год журнала: 2024, Номер 16(10), С. 1407 - 1407

Опубликована: Май 15, 2024

Artificial intelligence has undergone rapid development in the last thirty years and been widely used fields of materials, new energy, medicine, engineering. Similarly, a growing area research is use deep learning (DL) methods connection with hydrological time series to better comprehend expose changing rules these series. Consequently, we provide review latest advancements employing DL techniques for forecasting. First, examine application convolutional neural networks (CNNs) recurrent (RNNs) forecasting, along comparison between them. Second, made basic enhanced long short-term memory (LSTM) analyzing their improvements, prediction accuracies, computational costs. Third, performance GRUs, other models including generative adversarial (GANs), residual (ResNets), graph (GNNs), estimated Finally, this paper discusses benefits challenges associated forecasting using techniques, CNN, RNN, LSTM, GAN, ResNet, GNN models. Additionally, it outlines key issues that need be addressed future.

Язык: Английский

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Hybrid machine learning system based on multivariate data decomposition and feature selection for improved multitemporal evapotranspiration forecasting

Engineering Applications of Artificial Intelligence, Год журнала: 2024, Номер 135, С. 108744 - 108744

Опубликована: Июнь 3, 2024

Язык: Английский

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Robust drought forecasting in Eastern Canada: Leveraging EMD-TVF and ensemble deep RVFL for SPEI index forecasting

Expert Systems with Applications, Год журнала: 2024, Номер 256, С. 124900 - 124900

Опубликована: Июль 30, 2024

Язык: Английский

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Integrated Seasonal-Trend Decomposition Using Loess for Multi-Head Self-Attention Mechanism and Bidirectional Long Short-Term Memory Based Reference Evapotranspiration Prediction

Опубликована: Янв. 1, 2025

Язык: Английский

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STAT-LSTM: A multivariate spatiotemporal feature aggregation model for SPEI-based drought prediction

Earth Science Informatics, Год журнала: 2025, Номер 18(3)

Опубликована: Фев. 25, 2025

Язык: Английский

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A State-of-the-art Novel Approach to Predict Potato Crop Coefficient (Kc) by Integrating Advanced Machine Learning Tools

Smart Agricultural Technology, Год журнала: 2025, Номер unknown, С. 100896 - 100896

Опубликована: Март 1, 2025

Язык: Английский

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Innovative Multi-Temporal Evapotranspiration Forecasting Using Empirical Fourier Decomposition and Bidirectional Long Short-Term Memory

Smart Agricultural Technology, Год журнала: 2024, Номер unknown, С. 100619 - 100619

Опубликована: Окт. 1, 2024

Язык: Английский

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Runoff prediction using combined machine learning models and signal decomposition

Journal of Water and Climate Change, Год журнала: 2024, Номер 16(1), С. 230 - 247

Опубликована: Дек. 18, 2024

ABSTRACT Accurate forecasting of increasingly unpredictable river runoff is essential for effective water resource management in the face climate change and human activities. This study uses four machine learning models long short-term memory neural networks (LSTM), support vector (SVM), random forest, artificial network to improve accuracy explore combined models’ effectiveness. develops three advanced (empirical mode decomposition (EMD)–LSTM, VMD–LSTM, wavelet analysis (WA)–LSTM) by combining preprocessing techniques EMD, variational (VMD), WA with LSTM modeling method. These use signal analyze 41 years data from Huanren station (1980–2020). The findings reveal that model outperforms other individual when days high runoff. Among decomposed models, VMD–LSTM demonstrates best overall performance during validation period, achieving root mean square error, Nash–Sutcliffe efficiency coefficient, bias values 52.14 m3/s, 0.96, −0.002, respectively. combination shows promising potential enhancing prediction accuracy, practical implications flood control strategies.

Язык: Английский

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Adaptive Hybrid Potential Evapotranspiration (Pet) Prediction Method Based on Automatic Machine Learning

Опубликована: Янв. 1, 2025

In arid areas, estimation of crop water demand through potential evapotranspiration (PET) forecast has a guiding effect on water-saving irrigation, to cope with the crisis shortage. Neural network-based PET prediction methods is considered have huge application because its small error. However, physical conditions and data quality in different regions make choice neural network different, making it difficult provide general method. So an adaptive hybrid model based automatic machine learning for short-term proposed coupling formula. Process divided into two stages: forecasting. Learning stage includes three modules: meteorological reconstructing, set generation (PET calculation formula + network) selecting. Forecast rolling prediction. 105 standard weather stations Xinjiang were used as sets (43 them had missing data) test model. According modules, networks formulas process, corresponding labels generated each dataset result. Ratio training was 8:2. Grid search optimize best hyperparameter combination. set, average absolute error (MAE) squared (MSE) 0.338mm 0.270, achieving high accuracy. The mean smaller any single mixed We demonstrate that applicability varies among sources, Gate Recurrent Unit (GRU) 1 Dimension convolutional (1DCNN) are more suitable selected datasets, while Long Short Term Memory (LSTM) Multilayer Perceptron (MLP) not applicable. Combined analysis labels, find evidences independent geographic region degree drought. 2023, method 1-15 days verified, verification results show significantly than useing calculate PET. addition, by comparison,we determined input length can effectively reduce error, MAE 27.52% fixed length, MSE 45.76% length. realized forecast, predict accurately, be further expanded adding improve generalization ability.

Язык: Английский

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