Short-term flood probability density forecasting using a conceptual hydrological model with machine learning techniques-Reference-Cited by-同舟云学术

Short-term flood probability density forecasting using a conceptual hydrological model with machine learning techniques

Published:2022-01 Issue: Volume:604 Page:127255
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Zhou Yanlai,Cui Zhen,Lin Kangling,Sheng Sheng,Chen Hua,Guo Shenglian,Xu Chong-Yu

Funder

Wuhan University

National Key Research and Development Program of China

National Natural Science Foundation of China

Norges Forskningsråd

University of Oslo

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference58 articles.

1. Physics-informed neural networks with monotonicity constraints for Richardson-Richards equation: Estimation of constitutive relationships and soil water flux density from volumetric water content measurements;Bandai;Water Resour. Res.,2020

2. Atmospheric conservation properties in ERA-Interim;Berrisford;Q. J. R. Meteorol. Soc.,2011

3. Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes;Cannon;Stoch. Env. Res. Risk Assess.,2018

4. Quantile regression neural networks: Implementation in R and application to precipitation downscaling;Cannon;Comput. Geosci.,2011

5. Hydrologically informed machine learning for rainfall-runoff modeling: A Genetic Programming-Based Toolkit for automatic model induction;Chadalawada;Water Resour. Res.,2020

Cited by 53 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Improving flood forecast accuracy based on explainable convolutional neural network by Grad-CAM method;Journal of Hydrology;2024-10

2. Physics-informed geostatistical approach (PIGA) for real-time fluvial flood modeling;Journal of Hydrology;2024-10

3. Enhancing the streamflow simulation of a process-based hydrological model using machine learning and multi-source data;Ecological Informatics;2024-09

4. Enhancing Flood Risk Mitigation by Advanced Data-Driven Approach;Heliyon;2024-09

5. Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method;Hydrology and Earth System Sciences;2024-07-03