Peer-reviewedCC BY-NC-ND 4.0Scientific Reports
An interpretable model for efficient and robust runoff forecasting via causal-linear attention and adaptive KAN decoding
Related
- PhyTGNet: A Physics‐Prior‐Guided Interpretable Forecasting Method for Thunderstorm Gusts in the Beijing–Tianjin–Hebei Region
- A Hybrid Framework for Air Pollution Forecasting using DSES, DLA, Self-Attention Transformer, and BiGRU with Bayesian Optimization
- Precipitation nowcasting model based on multi-source data fusion and efficient additive attention
- Convolutional LSTM and Self-Attention Networks in PyTorch for Decade-Ahead Forecasting of Land-Cover Time Series
- Swift: an autoregressive consistency model for efficient weather forecasting
BibTeX
@article{tian2026interpretable,
title = {{An interpretable model for efficient and robust runoff forecasting via causal-linear attention and adaptive KAN decoding}},
author = {Wei-can Tian and Lin Cheng and Wen-chuan Wang},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-76565-y},
url = {https://www.nature.com/articles/s41598-026-76565-y},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - An interpretable model for efficient and robust runoff forecasting via causal-linear attention and adaptive KAN decoding
AU - Wei-can Tian
AU - Lin Cheng
AU - Wen-chuan Wang
PY - 2026
DA - 2026/10/18
JO - Scientific Reports
DO - 10.1038/s41598-026-76565-y
UR - https://www.nature.com/articles/s41598-026-76565-y
SN - 2045-2322
ER -