Evaluating uncertainty in integrated environmental models: A review of concepts and tools
LS Matott, JE Babendreier… - Water Resources …, 2009 - Wiley Online Library
This paper reviews concepts for evaluating integrated environmental models and discusses
a list of relevant software‐based tools. A simplified taxonomy for sources of uncertainty and …
a list of relevant software‐based tools. A simplified taxonomy for sources of uncertainty and …
Data-driven evolutionary optimization: An overview and case studies
Most evolutionary optimization algorithms assume that the evaluation of the objective and
constraint functions is straightforward. In solving many real-world optimization problems …
constraint functions is straightforward. In solving many real-world optimization problems …
A random forest-assisted evolutionary algorithm for data-driven constrained multiobjective combinatorial optimization of trauma systems
Many real-world optimization problems can be solved by using the data-driven approach
only, simply because no analytic objective functions are available for evaluating candidate …
only, simply because no analytic objective functions are available for evaluating candidate …
Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling
M Schartau, P Wallhead, J Hemmings, U Löptien… - …, 2017 - bg.copernicus.org
To describe the underlying processes involved in oceanic plankton dynamics is crucial for
the determination of energy and mass flux through an ecosystem and for the estimation of …
the determination of energy and mass flux through an ecosystem and for the estimation of …
[BOOK][B] Machine learning for spatial environmental data: theory, applications, and software
M Kanevski, V Timonin, A Pozdnukhov - 2009 - taylorfrancis.com
This book discusses machine learning algorithms, such as artificial neural networks of
different architectures, statistical learning theory, and Support Vector Machines used for the …
different architectures, statistical learning theory, and Support Vector Machines used for the …
Artificial neural networks as emulators of process-based models to analyse bathing water quality in estuaries
This study aims to provide a method for develo** artificial neural networks in estuaries as
emulators of process-based models to analyse bathing water quality and its variability over …
emulators of process-based models to analyse bathing water quality and its variability over …
A general framework for dynamic emulation modelling in environmental problems
Emulation modelling is an effective way of overcoming the large computational burden
associated with the process-based models traditionally adopted by the environmental …
associated with the process-based models traditionally adopted by the environmental …
[HTML][HTML] The history and practice of AI in the environmental sciences
Artificial intelligence (AI) and machine learning (ML) have become important tools for
environmental scientists and engineers, both in research and in applications. Although …
environmental scientists and engineers, both in research and in applications. Although …
Future digital twins: emulating a highly complex marine biogeochemical model with machine learning to predict hypoxia
J Skakala, K Awty-Carroll, PP Menon… - Frontiers in Marine …, 2023 - frontiersin.org
The Machine learning (ML) revolution is becoming established in oceanographic research,
but its applications to emulate marine biogeochemical models are still rare. We pioneer a …
but its applications to emulate marine biogeochemical models are still rare. We pioneer a …
Attention-based convolutional autoencoders for 3d-variational data assimilation
We propose a new 'Bi-Reduced Space'approach to solving 3D Variational Data Assimilation
using Convolutional Autoencoders. We prove that our approach has the same solution as …
using Convolutional Autoencoders. We prove that our approach has the same solution as …