Development of a sensitivity analysis framework for aquatic biogeochemical models using machine learning-Reference-Cited by-同舟云学术

Development of a sensitivity analysis framework for aquatic biogeochemical models using machine learning

Published:2023-07 Issue: Volume:75 Page:102079
ISSN:1574-9541
Container-title:Ecological Informatics
language:en
Short-container-title:Ecological Informatics

Author:

Cai Haibin,Shimoda Yuko,Mao Jingqiao,Arhonditsis George B.

Funder

Environment and Climate Change Canada Ontario Region

Department of the Environment, Australian Government

Mitacs

Government of Canada

China Scholarship Council

Publisher

Elsevier BV

Subject

Applied Mathematics,Computational Theory and Mathematics,Computer Science Applications,Ecological Modeling,Modeling and Simulation,Ecology,Ecology, Evolution, Behavior and Systematics

Reference163 articles.

1. The impact of prior parameter ranges on model behaviour using global sensitivity analysis;Almeida,2017

2. Assessment of self-organizing map artificial neural networks for the classification of sediment quality;Alvarez-Guerra;Environ. Int.,2008

3. Evaluation of the current state of mechanistic aquatic biogeochemical modeling;Arhonditsis;Mar. Ecol. Prog. Ser.,2004

4. Eutrophication model for Lake Washington (USA): part I. model description and sensitivity analysis;Arhonditsis;Ecol. Model.,2005

5. Patterns and mechanisms of phytoplankton variability in Lake Washington (USA);Arhonditsis;Water Res.,2004

Cited by 9 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. Stochastic effects on plankton dynamics: Insights from a realistic 0-dimensional marine biogeochemical model;Ecological Informatics;2024-11

2. Assessment of living quality in Guangdong: A hybrid knowledge-based and data-driven approach;Ecological Informatics;2024-09

3. Sensitivity analysis of transmission loss of side outlet muffler with conical joint;Mechanical Systems and Signal Processing;2024-09

4. Toward the development of an ecosystem model ensemble to support adaptive management in Lake Ontario;Environmental Reviews;2024-06-01

5. Development of AI-based hybrid soft computing models for prediction of critical river water quality indicators;Environmental Science and Pollution Research;2024-03-23