Fast hydrological model calibration based on the heterogeneous parallel computing accelerated shuffled complex evolution method-Reference-Cited by-同舟云学术

Fast hydrological model calibration based on the heterogeneous parallel computing accelerated shuffled complex evolution method

Published:2017-04-05 Issue:1 Volume:50 Page:106-119
ISSN:0305-215X
Container-title:Engineering Optimization
language:en
Short-container-title:Engineering Optimization

Author:

Kan Guangyuan¹²,He Xiaoyan¹,Ding Liuqian¹,Li Jiren¹,Hong Yang²³,Zuo Depeng⁴,Ren Minglei¹,Lei Tianjie¹,Liang Ke⁵

Affiliation:

1. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, Research Center on Flood & Drought Disaster Reduction of the Ministry of Water Resources, China Institute of Water Resources and Hydropower Research, Beijing, PR China

2. State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, PR China

3. Department of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK, USA

4. College of Water Sciences, Beijing Normal University, Beijing, PR China

5. College of Hydrology and Water Resources, Hohai University, Nanjing, PR China

Funder

National Natural Science Foundation of China

Major International (Regional) Joint Research Project—China’s Water and Food Security under Extreme Climate Change Impact: Risk Assessment and Resilience

Construction Project of Shaanxi Province Medium and Small River Hydrological Monitoring and Forecast System—Construction of Guanzhong and North of Shaanxi Flood Forecast Scheme

Study of Distributed Flood Risk Forecast Model and Technology Based on Multi-source Data Integration and Hydro Meteorological Coupling System

China National Flash Flood Disaster Prevention and Control Project

IWHR Research & Development Support Program

International Project

Publisher

Informa UK Limited

Subject

Applied Mathematics,Industrial and Manufacturing Engineering,Management Science and Operations Research,Control and Optimization,Computer Science Applications

Link

https://www.tandfonline.com/doi/pdf/10.1080/0305215X.2017.1303053

Reference37 articles.

1. Applying the ensemble artificial neural network-based hybrid data-driven model to daily total load forecasting

2. Duan, Q. 1991. A global optimization strategy for efficient and effective calibration of hydrologic models. Ph.D. thesis, Dept. of Hydrology and Water Resources, Univ. of Arizona, Tucson, AZ.

3. Shuffled complex evolution approach for effective and efficient global minimization

4. Effective and efficient global optimization for conceptual rainfall-runoff models

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