A Parsimonious Setup for Streamflow Forecasting using CNN-LSTM

2024/04/11 by Sudan Pokharel, Pokharel, Sudan, Tirthankar Roy +1
Environmental Science · #FOS: Computer and information sciences #Flood Risk Assessment and Management #Hydrological Forecasting Using AI #Hydrology and Watershed Management Studies #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2404.07924

openalex publication_date 2024/04/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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