1. Sustainable Marine Ecosystems: Deep Learning for Water Quality Assessment and Forecasting
- Author
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Angel Fernandez Gambin, Paolo Dini, Jesus Soriano Gonzalez, Eduard Angelats, Marco Miozzo, Universitat Politècnica de Catalunya. Doctorat en Ciència i Tecnologia Aeroespacials, and Universitat Politècnica de Catalunya. Doctorat en Tecnologia Agroalimentària i Biotecnologia
- Subjects
Coastal zone management ,Artificial intelligence ,Sustainable aquaculture ,Teledetecció ,General Computer Science ,Blue economy ,Computer science ,Big data ,Cloud computing ,water quality ,sustainable aquaculture ,remote sensing ,Machine learning ,Reinforcement learning ,General Materials Science ,blue economy ,Edge computing ,Sustainable development ,business.industry ,Intel·ligència artificial ,Sustainable coastal management ,Environmental resource management ,General Engineering ,Enginyeria de la telecomunicació [Àrees temàtiques de la UPC] ,Remote sensing ,Zones costaneres--Ordenació ,artificial intelligence ,Aqüicultura sostenible ,TK1-9971 ,Water quality ,machine learning ,Sustainable management ,Electrical engineering. Electronics. Nuclear engineering ,business ,Transfer of learning ,Coastal management - Abstract
An appropriate management of the available resources within oceans and coastal regions is vital to guarantee their sustainable development and preservation, where water quality is a key element. Leveraging on a combination of cross-disciplinary technologies including Remote Sensing (RS), Internet of Things (IoT), Big Data, cloud computing, and Artificial Intelligence (AI) is essential to attain this aim. In this paper, we review methodologies and technologies for water quality assessment that contribute to a sustainable management of marine environments. Specifically, we focus on Deep Leaning (DL) strategies for water quality estimation and forecasting. The analyzed literature is classified depending on the type of task, scenario and architecture. Moreover, several applications including coastal management and aquaculture are surveyed. Finally, we discuss open issues still to be addressed and potential research lines where transfer learning, knowledge fusion, reinforcement learning, edge computing and decision-making policies are expected to be the main involved agents.
- Published
- 2021