A framework of integrating heterogeneous data sources for monthly streamflow prediction using a state-of-the-art deep learning model-Reference-Cited by-同舟云学术

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A framework of integrating heterogeneous data sources for monthly streamflow prediction using a state-of-the-art deep learning model

Published:2022-11 Issue: Volume:614 Page:128599
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Xu Wenxin,Chen Jie,Zhang Xunchang J.,Xiong Lihua,Chen Hua

Funder

State Administration of Foreign Experts Affairs

Natural Science Foundation of Hubei Province

Overseas Expertise Introduction Project for Discipline Innovation

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

General Medicine

Reference77 articles.

1. Least square support vector machine and multivariate adaptive regression splines for streamflow prediction in mountainous basin using hydro-meteorological data as inputs;Adnan;Journal of Hydrology,2020

2. A novel attention-based LSTM cell post-processor coupled with bayesian optimization for streamflow prediction;Alizadeh;Journal of Hydrology,2021

3. Predictability and Forecast Skill in NMME;Becker;Journal of Climate,2014

4. Performance assessment of a Bayesian Forecasting System (BFS) for real-time flood forecasting;Biondi;Journal of Hydrology,2013

5. Long lead-time daily and monthly streamflow forecasting using machine learning methods;Cheng;Journal of Hydrology,2020

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