Exploring the best sequence LSTM modeling architecture for flood prediction
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ORIGINAL ARTICLE
Exploring the best sequence LSTM modeling architecture for flood prediction Wei Li1 • Amin Kiaghadi1
•
Clint Dawson1
Received: 28 May 2020 / Accepted: 2 September 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Accurate and efficient models for rainfall–runoff (RR) simulations are crucial for flood risk management. Recently, the success of the recurrent neural network (RNN) applied to sequential models has motivated groups to pursue RR modeling using RNN. Existing RNN based methods generally use either sequence input single output or unsynced sequence input and output architectures. In this paper, we propose a synced sequence input and output long short-term memory (LSTM) network architecture for hydrologic analysis and compare it to existing methods (sequence input single output LSTM). We expect the model will improve RR prediction in terms of accuracy, calibration training time, and computational cost. The key idea is to efficiently learn the long term dependency of runoff on past rainfall history. To be more specific, we use the indigenous ability of the LSTM network to preserve long term memory instead of artificially setting a time window for input data. In this way, we can avoid losing long term memory of the input, the calibration of the time window length, and excessive computation. The whole procedure mimics the traditional process-driven methods and is closer to the physics interpretation of the RR process. We conducted experiments on real-world hydrologic data from the Brays Bayou in Houston, Texas. Extensive experimental results clearly validate the effectiveness of our proposed method in terms of various statistical and hydrological related evaluation metrics. Notably, our experiment shows that some rainfall events could affect the runoff process in the test watershed for at least a week. For fine temporal resolution prediction, this long term effect needs to be carefully handled, and our proposed method is superior in this case. Keywords Recurrent neural network Hydrologic analysis Sequence modeling Long short-term memory network
1 Introduction In the United States, flooding is the number one cause of natural-disaster losses with estimated annual damage of eight billion dollars [1]. Thus, reliable predictive tools for rainfall–runoff (RR) modeling are crucial for flood prevention, mitigation, and management. The literature is replete with studies using different modeling approaches to predict runoff (discharge) caused by a rainfall event [2–5]. Both process-driven [6–8] and data-driven [9, 10] approaches were applied in this research area. The results of the process-driven models are more realistic and scalable due to the use of analytical and empirical formulae based on & Amin Kiaghadi [email protected] 1
Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX 78712, USA
physical phenomena. However, extensive meteorological and geometric data requiremen
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