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Remote Sens. 2017, 9(12), 1320; doi:10.3390/rs9121320

Construction of Multi-Year Time-Series Profiles of Suspended Particulate Inorganic Matter Concentrations Using Machine Learning Approach

1
Service Hydrographique et Océanographique de la Marine (SHOM), 29228 Brest, France
2
Conservatoire National des Arts et Métiers (CNAM), 75003 Paris, France
3
École Nationale Supérieure d’Informatique pour l’Industrie et l’Entreprise (ENSIIE), 91000 Évry, France
4
Cerema, Direction Eau Mer et Fleuves, ER, Laboratoire de Génie Côtier et Environnement, Technopôle Brest-Iroise, 29280 Plouzané, France
5
IFREMER, Centre de Bretagne, Technople Brest-Iroise, 29280 Plouzané, France
6
Laboratoire d’Océanographie et du Climat: Expérimentations et Approches Numériques (LOCEAN), 75005 Paris, France
*
Author to whom correspondence should be addressed.
Received: 22 September 2017 / Revised: 1 December 2017 / Accepted: 8 December 2017 / Published: 15 December 2017
(This article belongs to the Special Issue Remote Sensing of Water Quality)
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Abstract

Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used a computationally less-expensive, well-established methodology of self-organizing maps (SOMs) along with a hidden Markov model (HMM) to derive profiles of suspended particulate inorganic matter (SPIM). The concept of the proposed work is to benefit from all available data sets through the use of fusion methods and machine learning approaches that are able to process a growing amount of available data. This approach is applied to two different data sets entitled “Hidden” and “Observable”. The hidden data are composed of 15 months (27 September 2007 to 30 December 2008) of hourly SPIM profiles extracted from the Regional Ocean Modeling System (ROMS). The observable data include forcing parameter variables such as significant wave heights ( H s and H s 50 (50 days)) from the Wavewatch 3-HOMERE database and barotropic currents ( U b a r and V b a r ) from the Iberian–Biscay–Irish (IBI) reanalysis data. These observable data integrate hourly surface samples from 1 February 2002 to 31 December 2012. The time-series profiles of the SPIM have been derived from four different stations in the English Channel by considering 15 months of output hidden data from the ROMS as a statistical representation of the ocean for ≈11 years. The derived SPIM profiles clearly show seasonal and tidal fluctuations in accordance with the parent numerical model output. The surface SPIM concentrations of the derived model have been validated with satellite remote sensing data. The time series of the modeled SPIM and satellite-derived SPIM show similar seasonal fluctuations. The ranges of concentrations for the four stations are also in good agreement with the corresponding satellite data. The high accuracy of the estimated 25 h average surface SPIM concentrations (normalized root-mean-square error— N R M S E of less than 16%) is the first step in demonstrating the robustness of the method. View Full-Text
Keywords: suspended particulate inorganic matter; self-organizing maps; Hidden Markov Model; machine learning; English Channel; ROMS suspended particulate inorganic matter; self-organizing maps; Hidden Markov Model; machine learning; English Channel; ROMS
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Renosh, P.R.; Jourdin, F.; Charantonis, A.A.; Yala, K.; Rivier, A.; Badran, F.; Thiria, S.; Guillou, N.; Leckler, F.; Gohin, F.; Garlan, T. Construction of Multi-Year Time-Series Profiles of Suspended Particulate Inorganic Matter Concentrations Using Machine Learning Approach. Remote Sens. 2017, 9, 1320.

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