Version 1
: Received: 13 August 2024 / Approved: 14 August 2024 / Online: 16 August 2024 (03:36:37 CEST)
How to cite:
Tepetidis, N.; Koutsoyiannis, D.; Iliopoulou, T.; Dimitriadis, P. Investigating the Performance of the Informer Model for Streamflow Forecasting. Preprints2024, 2024081084. https://doi.org/10.20944/preprints202408.1084.v1
Tepetidis, N.; Koutsoyiannis, D.; Iliopoulou, T.; Dimitriadis, P. Investigating the Performance of the Informer Model for Streamflow Forecasting. Preprints 2024, 2024081084. https://doi.org/10.20944/preprints202408.1084.v1
Tepetidis, N.; Koutsoyiannis, D.; Iliopoulou, T.; Dimitriadis, P. Investigating the Performance of the Informer Model for Streamflow Forecasting. Preprints2024, 2024081084. https://doi.org/10.20944/preprints202408.1084.v1
APA Style
Tepetidis, N., Koutsoyiannis, D., Iliopoulou, T., & Dimitriadis, P. (2024). Investigating the Performance of the Informer Model for Streamflow Forecasting. Preprints. https://doi.org/10.20944/preprints202408.1084.v1
Chicago/Turabian Style
Tepetidis, N., Theano Iliopoulou and Panayiotis Dimitriadis. 2024 "Investigating the Performance of the Informer Model for Streamflow Forecasting" Preprints. https://doi.org/10.20944/preprints202408.1084.v1
Abstract
Recent studies have shown the potential of Transformer-based neural networks in increasing prediction capacity. However, classical transformers present several problems such as computational time complexity and high memory requirements, which make Long Sequence Time-Series Forecasting (LSTF) challenging. The contribution to the prediction of time series of flood events using deep learning techniques is examined, with a particular focus on evaluating the performance of the Informer model (a particular implementation of transformer architecture), which attempts to address the previous issues. This study explores the predictive capabilities of the Informer model compared to statistical methods, stochastic models and traditional deep neural networks. The accuracy, efficiency as well as the limits of the approaches are demonstrated via numerical benchmarks relating to real river streamflow applications. Using daily flow data from the River Test in England as the main case study, we conduct a rigorous evaluation of the Informer efficacy in capturing the complex temporal dependencies inherent in flood time series. Among other things, the present work extends its analysis to encompass diverse time series datasets from various locations (>100) in the United Kingdom, providing insights into the generalizability of the Informer. The results highlight the significant superiority of the Informer model over established forecasting methods, especially regarding the LSTF problem. Furthermore, it is observed that the structure of time series, as expressed by climacogram, affects the performance of the Informer network.
Keywords
flood; river streamflow forecasting; long sequence time series forecasting (LSTF); deep learning; transformers; attention mechanism; Informer model
Subject
Environmental and Earth Sciences, Water Science and Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
