Abstract
Machine learning has a special place in data science among researchers and scientists nowadays. Machine learning contains algorithms and models which permit computer systems to explore patterns in data. It is quite challenging and difficult for traditional machine learning techniques to obtain information and pattern from big and complex data. As a subset of machine learning or even artificial intelligence, deep learning focuses on developing large network architectures to make suitable and accurate data-driven decisions. Deep learning architectures contain multiple hidden layers (deep network) to learn different features from complex and extensive datasets. In such datasets, deep learning algorithms explore the unknown datasets and structures to identify valuable relationships. Deep learning has shown its capability in different water and environmental sectors and represents itself as an appropriate model, particularly in modeling large (big) and complex datasets. This chapter provides a review of deep learning concepts, introduce some of the developed deep learning structures, and their application in water and environmental studies.
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Senior co-author, Professor Francisco Martínez-Álvarez, would like to thank the Spanish Ministry of Economy and Competitiveness for the support under the projects TIN2017-88209-C2-1-R and PID2020-11795RB-C21.
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Yaghoubzadeh-Bavandpour, A., Bozorg-Haddad, O., Zolghadr-Asli, B., Martínez-Álvarez, F. (2022). Deep Learning Application in Water and Environmental Sciences. In: Bozorg-Haddad, O., Zolghadr-Asli, B. (eds) Computational Intelligence for Water and Environmental Sciences. Studies in Computational Intelligence, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-19-2519-1_13
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