A new few-shot learning model for runoff prediction: Demonstration in two data scarce regions. (April 2023)

• Record Type: Journal Article
• Authors: Yang, Minghong; Yang, Qinli; Shao, Junming; Wang, Guoqing; Zhang, Wei
• Is Part Of: Environmental modelling & software. Volume 162(2023)
• Publication Date: 2023-04
• Subjects: Runoff prediction -- Few-shot learning -- LSTM -- Prototypical network -- Sparsely-gauged basins; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2023.105659
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Most existing hydrologic models and machine learning models failed to perform well on runoff prediction in data scarce regions. As an alternative to this, the Long Short-Term Memory (LSTM)-prototypical network fusion model based on few-shot learning is proposed, where the strong learning ability of LSTM and the low data dependence of prototypical network are combined. The proposed model was calibrated and implemented on monthly runoff prediction in the Lancang River basin (LRB) and the source region of the Yellow River basin (SRYRB). Compared with eight state-of-the-art data driven models (LSTM, SVR, ANN, ARMA, Random Forest, SimpleRNN, GRU, and BiLSTM), the proposed model outperformed especially when less training data were used. Results in the LRB indicate NSE of the proposed model achieved 0.802 and 0.832 when the proportion of training data ( K ) was 20% and 45%, improved by 0.527 and 0.222 relative to the mean NSE of other models, respectively. In the SRYRB, NSE reached 0.830 and improved by 0.354 when K was 40%. The findings imply that the new few-shot learning model provides a promising tool for runoff prediction in the two investigated basins and possibly other data-scarce basins where precipitation dominates runoff change, which will benefit regional water resources management and water security. Highlights: LSTM-prototypical network fusion model is proposed for monthly runoff prediction. NSE ranges from 0.802 to 0.832 when training data proportion increases from 20% to 45% in LRB. NSE is improved by 0.212–0.527 relative to the mean of 8 comparative data driven models. The model has lower data dependance, suitable for data scarce regions where rainfall dominates runoff change.