Artificial Intelligence techniques: An introduction to their use for modelling environmental systems

By Serena H. Chen, Anthony J. Jakeman, John P. Norton. Mathematics and Computers in Simulation, 2008

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Machine learning and deep learning (2021, background): “reasoning, rule-based systems, genetic algorithms, fuzzy models, multi-agent systems) (Chen et al. 2008).”
A review of artificial neural network models for ambient air pollution prediction (2019, methodology): “The use of hybrid modelling approaches has also been practised by modellers given their high potential at improving the performance of plain ANN models (Chen et al., 2008; Sharma et al., 2005).”
Artificial intelligence in supply chain management: A systematic literature review (2021, unknown): “This is mainly due to their capability of solving data-intensive problems in which the rules or algorithms for solving the problem are unknown or difficult to express (Chen et al., 2008).”
Selecting among five common modelling approaches for integrated environmental assessment and management (2013, methodology): “What are knowledge-based models? Traditionally, in this type of model knowledge is encoded into a knowledge base and then an inference engine uses logic to infer conclusions (Chen et al., 2008; Davis, 1995; Davis et al., 1992).”
Physical and hybrid methods comparison for the day ahead PV output power forecast (2017, background): “The correct functioning of a neural network depends first of all on the architecture of the network [5] (determination of the layers number and of the neurons number in every layer) and on the choice of the activation function.”