Deep Learning for Twelve Hour Precipitation Forecasts,Nature Communication 2022 [PDF] [BLOG] [CODE] Deep Learning for Day Forecasts from Sparse Observations,arXiv 2023 [PDF] MetNet v1-v3的性能对比以及对照的物理模型 一、降水预报可用的数据 可能有小伙伴对于降水预报任务还不太熟悉,本章节监督基于MetNet...
Traditional physically based models are hampered by sparse parameters and complex calibration procedures particularly in ungauged catchments. We propose a novel hybrid deep learning model termed encoder-decoder double-layer long short-term memory (ED-DLSTM) to address streamflow forecasting at global ...
Deep learning extracts patterns and forecasts from massive datasets employing artificial neural networks. Deep learning has the primary advantage of eliminating the need for tedious human feature engineering by automatically extracting features from raw data. Because of their ability to handle massive ...
Motor Imagery Deep fusion feature learning network for MI-EEG classification. CNN, LSTM (DWT) IEEE Access 2018 Motor Imagery LSTM-based EEG classification in motor imagery tasks. LSTM IEEE Trans. Neural Syst. Rehabil. Eng. 2018 Motor Imagery EEG classification using sparse Bayesian extreme learning...
Fernando T, Denman S, Sridharan S, Fookes C (2021) Deep inverse reinforcement learning for behavior prediction in autonomous driving: accurate forecasts of vehicle motion. IEEE Signal Process Mag 38(1):87–96.https://doi.org/10.1109/MSP.2020.2988287 ...
The most important goal for people trading in stock markets is to earn a profit. This profit can be obtained from price increases or decreases in a two-sid
When the forecasts are compared to the actual situation, they could be classified as true positive (TP), false positive (FP), true negative (TN), or false negative (FN). Further, the overall accuracy, recall, and precision within a single session could be calculated (Fig. S1). Because ...
Forecasting the evolution of contagion dynamics is still an open problem to which mechanistic models only offer a partial answer. To remain mathematically or computationally tractable, these models must rely on simplifying assumptions, thereby limiting t
Existing methods for fine-scale air quality assessment have significant gaps in their reliability. Purely data-driven methods lack any physically-based mechanisms to simulate the interactive process of air pollution, potentially leading to physically inc
Furthermore, the TgDLF is built based on the LSTM, in which all potentially useful information of the entire series is automatically extracted without the need for complex feature engineering. Furthermore, from a machine learning perspective, gradient-free is a desirable characteristic since it has...