A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Sustainable Marine Ecosystems: Deep Learning for Water Quality Assessment and Forecasting
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. ; © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sustainable Marine Ecosystems: Deep Learning for Water Quality Assessment and Forecasting
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. ; © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sustainable Marine Ecosystems: Deep Learning for Water Quality Assessment and Forecasting
Fernández Gambín, Ángel (author) / Angelats, Eduard (author) / Soriano González, Jesús (author) / Miozzo, Marco (author) / Dini, Paolo (author)
2021-09-01
oai:zenodo.org:5521025
IEEE Access 9 121344 - 121365
Article (Journal)
Electronic Resource
English
DDC:
710
Forecasting air quality time series using deep learning
| Taylor & Francis Verlag | 2018
Australia’s approach to the conservation and sustainable use of marine ecosystems
| Taylor & Francis Verlag | 2012
Attention-Based Distributed Deep Learning Model for Air Quality Forecasting
| DOAJ | 2022
Towards Sustainable Urban Ecosystems
| British Library Conference Proceedings | 2002
Multi-step tap-water quality forecasting in South Korea with transformer-based deep learning model
| Taylor & Francis Verlag | 2024