A new few-shot learning model for runoff prediction: Demonstration in two data scarce regions-Reference-Cited by-同舟云学术

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A new few-shot learning model for runoff prediction: Demonstration in two data scarce regions

Published:2023-04 Issue: Volume:162 Page:105659
ISSN:1364-8152
Container-title:Environmental Modelling & Software
language:en
Short-container-title:Environmental Modelling & Software

Author:

Yang Minghong^ORCID,Yang Qinli,Shao Junming,Wang Guoqing,Zhang Wei

Funder

National Natural Science Foundation of China

State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering

State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin

Fundamental Research Funds for the Central Universities

Sichuan Province Science and Technology Support Program

Publisher

Elsevier BV

Subject

Ecological Modeling,Environmental Engineering,Software

Reference66 articles.

1. Evaluation of stochastic and artificial intelligence models in modeling and predicting of river daily flow time series;Aghelpour;Stoch. Environ. Res. Risk Assess.,2020

2. Predicting Runoff Series in Ungauged Basins of the Brazilian Cerrado Biome;Althoff,2022

3. On the optimality of conditional expectation as a Bregman predictor;Banerjee;IEEE Trans. Inf. Theor.,2005

4. Auto-regressive neural-network models for long lead-time forecasting of daily flow;Banihabib;Water Resour. Manag.,2019

5. Characterising performance of environmental models;Bennett;Environ. Model. Software,2013

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4. An Improved Anticipated Learning Machine for Daily Runoff Prediction in Data-Scarce Regions;Mathematical Geosciences;2024-08-10

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