Cited by Soil temperature estimation at different depths using machine learning paradigms based on meteorological data

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

Xinfeng Zhao, Hongyan Wang,

Mingyu Bai

et al.

Water, Journal Year: 2024, Volume and Issue: 16(10), P. 1407 - 1407

Published: May 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.

Language: Английский

Citations

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

Jinwook Lee, Sayed M. Bateni, Changhyun Jun

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 135, P. 108744 - 108744

Published: June 3, 2024

Language: Английский

Citations

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

Zehai Gao, Zijun Gao, Xiaojun Zhang

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

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

Ying Chen,

Huanping Wu,

Nengfu Xie

et al.

Earth Science Informatics, Journal Year: 2025, Volume and Issue: 18(3)

Published: Feb. 25, 2025

Language: Английский

Citations

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

Menghang Li,

Qingyun Zhou,

Xin Han

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132223 - 132223

Published: Oct. 1, 2024

Language: Английский

Citations

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

Masoud Karbasi, Mumtaz Ali, Aitazaz A. Farooque

et al.

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 256, P. 124900 - 124900

Published: July 30, 2024

Language: Английский

Citations

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

宸嘉张,

Tianxin Xu,

Yan Zhang

et al.

Published: Jan. 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.

Language: Английский

Citations

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

Saad Javed Cheema, Masoud Karbasi,

Gurjit S. Randhawa

et al.

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100896 - 100896

Published: March 1, 2025

Language: Английский

Citations

Innovative application of the composite Bezier GSXG hybrid machine learning model for daily evapotranspiration Estimation implementing satellite image data DOI

Parastoo Amirzehni,

Saeed Samadianfard,

AmirHossein Nazemi

et al.

Environmental Earth Sciences, Journal Year: 2025, Volume and Issue: 84(9)

Published: April 18, 2025

Language: Английский

Citations

Prediction method for instrument transformer measurement error: Adaptive decomposition and hybrid deep learning models DOI

Zhenhua Li,

Jiuxi Cui,

Heping Lu

et al.

Measurement, Journal Year: 2025, Volume and Issue: unknown, P. 117592 - 117592

Published: May 1, 2025

Language: Английский

Citations