FN Archimer Export Format
PT J
TI Construction of Multi-Year Time-Series Profiles of Suspended Particulate Inorganic Matter Concentrations Using Machine Learning Approach
BT 
AF RENOSH, Pannimpullath R.
   JOURDIN, Frederic
   CHARANTONIS, Anastase A.
   YALA, Khalil
   RIVIER, Aurelie
   BADRAN, Fouad
   THIRIA, Sylvie
   GUILLOU, Nicolas
   LECKLER, Fabien
   GOHIN, Francis
   GARLAN, Thierry
AS 1:1,2;2:1;3:3;4:2;5:4,5;6:2;7:6;8:4;9:1;10:5;11:1;
FF 1:;2:;3:;4:;5:;6:;7:;8:;9:PDG-ODE-LOS;10:PDG-ODE-DYNECO-PELAGOS;11:;
C1 SHOM, F-29228 Brest, France.
   CNAM, F-75003 Paris, France.
   ENSIIE, F-91000 Evry, France.
   Cerema, Direct Eau Mer & Fleuves, Lab Genie Cotier & Environm, ER, Technopole Brest Iroise, F-29280 Plouzane, France.
   IFREMER, Ctr Bretagne, Technople Brest Iroise, F-29280 Plouzane, France.
   LOCEAN, F-75005 Paris, France.
C2 SHOM, FRANCE
   CNAM, FRANCE
   ENSIIE, FRANCE
   CEREMA, FRANCE
   IFREMER, FRANCE
   UNIV PARIS 06, FRANCE
SI BREST
SE PDG-ODE-LOS
   PDG-ODE-DYNECO-PELAGOS
UM LOPS
IN WOS Ifremer jusqu'en 2018
   DOAJ
   copubli-france
   copubli-univ-france
IF 3.406
TC 10
UR https://archimer.ifremer.fr/doc/00415/52653/53511.pdf
LA English
DT Article
DE ;suspended particulate inorganic matter;self-organizing maps;Hidden Markov Model;machine learning;English Channel;ROMS
AB 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 (Hs and Hs50 (50 days)) from the Wavewatch 3-HOMERE database and barotropic currents (Ubar and Vbar) 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—NRMSE of less than 16%) is the first step in demonstrating the robustness of the method. 
PY 2017
PD DEC
SO Remote Sensing
SN 2072-4292
PU Mdpi Ag
VL 9
IS 12
UT 000419235700115
DI 10.3390/rs9121320
ID 52653
ER

EF