Attention-based Domain Adaptation Forecasting of Streamflow in Data-Sparse Regions

• URL: http://arxiv.org/abs/2302.05386v3
• Date: Mon, 17 Apr 2023 15:36:04 GMT
• Title: Attention-based Domain Adaptation Forecasting of Streamflow in Data-Sparse Regions
• Authors: Roland Oruche, Fearghal O'Donncha
• Abstract summary: We propose an attention-based domain adaptation streamflow forecaster for data-sparse regions. Our approach leverages the hydrological characteristics of a data-rich source domain to induce effective 24hr lead-time streamflow prediction.
• Score: 0.40285032034172336
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Streamflow forecasts are critical to guide water resource management, mitigate drought and flood effects, and develop climate-smart infrastructure and governance. Many global regions, however, have limited streamflow observations to guide evidence-based management strategies. In this paper, we propose an attention-based domain adaptation streamflow forecaster for data-sparse regions. Our approach leverages the hydrological characteristics of a data-rich source domain to induce effective 24hr lead-time streamflow prediction in a data-constrained target domain. Specifically, we employ a deep-learning framework leveraging domain adaptation techniques to simultaneously train streamflow predictions and discern between both domains using an adversarial method. Experiments against baseline cross-domain forecasting models show improved performance for 24hr lead-time streamflow forecasting.

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