Abstract
This chapter reviews the use of Evolutionary Machine Learning (EML) in environmental science. We cover the various steps of the machine learning pipeline, also addressing topics like model robustness, interpretability, and human-competitiveness. Environmental science is an interdisciplinary field mainly dedicated to climate change, natural resource management, conservation biology, and sustainability. We review applications such as forest monitoring, optimization of photovoltaic installations, improvement of traffic flow, and reduction of waste in animal farms, among others.
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Frequently also called feature extraction, feature generation, feature learning, feature discovery, feature synthesis, or constructive induction.
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This work was supported by the FCT, Portugal, through funding of the LASIGE Research Unit (UIDB/00408/2020 and UIDP/00408/2020); João Batista was supported by PhD grant SFRH/BD/143972/2019.
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Batista, J.E., Silva, S. (2024). Evolutionary Machine Learning in Environmental Science. In: Banzhaf, W., Machado, P., Zhang, M. (eds) Handbook of Evolutionary Machine Learning. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-99-3814-8_19
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