A Parsimonious Setup for Streamflow Forecasting using CNN-LSTM
<|reference_start|>A Parsimonious Setup for Streamflow Forecasting using CNN-LSTM: Significant strides have been made in advancing streamflow predictions, notably with the introduction of cutting-edge machine-learning models. Predominantly, Long Short-Term Memories (LSTMs) and Convolution Neural Networks (CNNs) have been widely employed in this domain. While LSTMs are applicable in both rainfall-runoff and time series settings, CNN-LSTMs have primarily been utilized in rainfall-runoff scenarios. In this study, we extend the application of CNN-LSTMs to time series settings, leveraging lagged streamflow data in conjunction with precipitation and temperature data to predict streamflow. Our results show a substantial improvement in predictive performance in 21 out of 32 HUC8 basins in Nebraska, showcasing noteworthy increases in the Kling-Gupta Efficiency (KGE) values. These results highlight the effectiveness of CNN-LSTMs in time series settings, particularly for spatiotemporal hydrological modeling, for more accurate and robust streamflow predictions.<|reference_end|>
@article{pokharel2024a,
    title={A Parsimonious Setup for Streamflow Forecasting using CNN-LSTM},
    author={Sudan Pokharel, Tirthankar Roy},
    journal={arXiv preprint arXiv:2404.07924},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07924},
    primaryClass={cs.LG}
}