scholarly journals SCADA-based neural network thrust load model for fatigue assessment: cross validation with in-situ measurements

2020 ◽  
Vol 1618 ◽  
pp. 022020
Author(s):  
Francisco d N Santos ◽  
Nymfa Noppe ◽  
Wout Weijtjens ◽  
Christof Devriendt
Keyword(s):  
Neural Network ◽  
Cross Validation ◽  
In Situ Measurements ◽  
Fatigue Assessment ◽  
Load Model ◽  
Thrust Load

New insights into nutrients dynamics and the carbonate system using a neural network approach in the Mediterranean Sea

2020 ◽  
Author(s):  
Marine Fourrier ◽  
Laurent Coppola ◽  
Fabrizio D'Ortenzio
Keyword(s):  
Neural Network ◽  
Mediterranean Sea ◽  
Dissolved Inorganic Carbon ◽  
In Situ Measurements ◽  
Total Alkalinity ◽  
Anthropogenic Pressure ◽  
Carbonate System ◽  
The Mediterranean ◽  
The Impact

<p>The semi-enclosed nature of the Mediterranean Sea, together with its small inertia which is due to the relatively short residence time of its water masses, make it highly reactive to external forcings and anthropogenic pressure. In this context, several rapid changes have been observed in physical and biogeochemical processes in recent decades, partly masked by episodic events and high regional variability. To better understand the underlying processes driving the Mediterranean evolution and, anticipate changes, the measurement, and integration of many biogeochemical variables are mandatory.</p><p>The development of new BGC sensors implemented on <em>in situ</em> autonomous platforms allows to increase the acquisition of essential biogeochemical variables. However, the measurements carried out by<em> in situ</em> autonomous platforms (e.g. profiling floats, gliders, moorings) are not exhaustive.</p><p>Recently, deep learning techniques and in particular neural networks have been developed. The CANYON-MED (for Carbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural-network in the MEDiterranean Sea) neural network-based method provides estimations of nutrients (i.e. nitrates, phosphates, and silicates) and carbonate system variables (i.e. total alkalinity, dissolved inorganic carbon, pH<sub>T</sub>) from systematically measured oceanographic variables such as in situ measurements of pressure, temperature, salinity, and oxygen together with geolocation and date of sampling.</p><p>This regional approach, therefore, using quality-controlled in situ measurements from more than 35 cruises. CANYON-MED obtains satisfactory results: accuracies of 0.73, 0.045, and 0.70 µmol.kg<sup>-1</sup> for the nitrates, phosphates and silicates concentrations respectively, and 0.016, 11 µmol.kg<sup>-1</sup> and 10 µmol.kg<sup>-1</sup> for pH<sub>T</sub>, total alkalinity and dissolved organic carbon respectively. CANYON-MED thus generates “virtual” data of parameters not yet measured by autonomous platforms, while ably reproducing the data already sampled, emphasizing its ability to fill the gaps in time-series.</p><p>Hence, by applying it to the large and growing network of autonomous platforms in the Mediterranean Sea, this method allows us to gain new insights into nutrients and carbonate system dynamics in targeted areas. In particular, in the northwestern Mediterranean Sea, the impact of deep convection on biogeochemistry (e.g., nutrient replenishment and pH<sub>T</sub> variability) is highly variable over time and poorly covered by observing networks. In this case, CANYON-MED would improve our observations and understanding of the dynamic and coupled system.</p>

scholarly journals Comparing the Performance of Neural Network and Deep Convolutional Neural Network in Estimating Soil Moisture from Satellite Observations

2018 ◽  
Vol 10 (9) ◽  
pp. 1327 ◽  
Author(s):  
Lingling Ge ◽  
Renlong Hang ◽  
Yi Liu ◽  
Qingshan Liu
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Convolutional Neural Network ◽  
Hydrological Cycle ◽  
In Situ Measurements ◽  
Learning Phase ◽  
Satellite Observations ◽  
Deep Convolutional Neural Network ◽  
Testing Phase

Soil moisture (SM) plays an important role in hydrological cycle and weather forecasting. Satellite provides the only viable approach to regularly observe large-scale SM dynamics. Conventionally, SM is estimated from satellite observations based on the radiative transfer theory. Recent studies have demonstrated that the neural network (NN) method can retrieve SM with comparable accuracy as conventional methods. Here, we are interested in whether the NN model with more complex structures, namely deep convolutional neural network (DCNN), can bring about further improvement in SM retrievals when compared with the NN model used in recent studies. To achieve this objective, the same input data are used for the DCNN and NN models, including L-band Soil Moisture and Ocean Salinity (SMOS) brightness temperature (TB), C-band Advanced Scatterometer (ASCAT) backscattering coefficients, Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and soil temperature. The target SM used to train the DCNN and NN models is the European Center for Medium-range Weather Forecasts Re-Analysis Interim (ERA-Interim) product. The experiment consists of two phases: the learning phase from 1 January to 31 December 2015 and the testing phase from 1 January to 31 December 2016. In the learning phase, we train the DCNN and NN models using the ERA-Interim SM. When evaluation between DCNN and NN against in situ measurements in the testing phase, we find that the temporal correlations between DCNN SM and in situ measurements are higher than those between NN SM and in situ measurements by 6 . 2 % and 2 . 5 % on ascending and descending orbits, respectively. In addition, from the perspective of temporal and spatial dynamics, the simulated SM values by DCNN/NN and the ERA-Interim SM agree relatively well at a global scale. Results suggest that both NN and DCNN models are effective in estimating SM from satellite observations, and DCNN can achieve slightly better performance than NN.

scholarly journals Sensitive Feature Evaluation for Soil Moisture Retrieval Based on Multi-Source Remote Sensing Data with Few In-Situ Measurements: A Case Study of the Continental U.S.

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2003
Author(s):  
Ling Zhang ◽  
Zixuan Zhang ◽  
Zhaohui Xue ◽  
Hao Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Feature Selection ◽  
Soil Moisture ◽  
Remote Sensing Data ◽  
In Situ Measurements ◽  
Feature Subset ◽  
Selection Methods ◽  
Sensing Data

Soil moisture (SM) plays an important role for understanding Earth’s land and near-surface atmosphere interactions. Existing studies rarely considered using multi-source data and their sensitiveness to SM retrieval with few in-situ measurements. To solve this issue, we designed a SM retrieval method (Multi-MDA-RF) using random forest (RF) based on 29 features derived from passive microwave remote sensing data, optical remote sensing data, land surface models (LSMs), and other auxiliary data. To evaluate the importance of different features to SM retrieval, we first compared 10 filter or embedded type feature selection methods with sequential forward selection (SFS). Then, RF was employed to establish a nonlinear relationship between the in-situ SM measurements from sparse network stations and the optimal feature subset. The experiments were conducted in the continental U.S. (CONUS) using in-situ measurements during August 2015, with only 5225 training samples covering the selected feature subset. The experimental results show that mean decrease accuracy (MDA) is better than other feature selection methods, and Multi-MDA-RF outperforms the back-propagation neural network (BPNN) and generalized regression neural network (GRNN), with the R and unbiased root-mean-square error (ubRMSE) values being 0.93 and 0.032 cm3/cm3, respectively. In comparison with other SM products, Multi-MDA-RF is more accurate and can well capture the SM spatial dynamics.

The Carbon:²³⁴Thorium ratios of sinking particles in the California Current Ecosystem 2: Examination of a thorium sorption, desorption, and particle transport model

2019 ◽  
Author(s):  
Michael Stukel ◽  
Thomas Kelly
Keyword(s):  
Organic Carbon ◽  
Water Column ◽  
Particulate Organic Carbon ◽  
Transport Model ◽  
California Current ◽  
In Situ Measurements ◽  
Power Law Distribution ◽  
Sinking Particles ◽  
Organic Carbon Concentration

Thorium-234 (234Th) is a powerful tracer of particle dynamics and the biological pump in the surface ocean; however, variability in carbon:thorium ratios of sinking particles adds substantial uncertainty to estimates of organic carbon export. We coupled a mechanistic thorium sorption and desorption model to a one-dimensional particle sinking model that uses realistic particle settling velocity spectra. The model generates estimates of 238U-234Th disequilibrium, particulate organic carbon concentration, and the C:234Th ratio of sinking particles, which are then compared to in situ measurements from quasi-Lagrangian studies conducted on six cruises in the California Current Ecosystem. Broad patterns observed in in situ measurements, including decreasing C:234Th ratios with depth and a strong correlation between sinking C:234Th and the ratio of vertically-integrated particulate organic carbon (POC) to vertically-integrated total water column 234Th, were accurately recovered by models assuming either a power law distribution of sinking speeds or a double log normal distribution of sinking speeds. Simulations suggested that the observed decrease in C:234Th with depth may be driven by preferential remineralization of carbon by particle-attached microbes. However, an alternate model structure featuring complete consumption and/or disaggregation of particles by mesozooplankton (e.g. no preferential remineralization of carbon) was also able to simulate decreasing C:234Th with depth (although the decrease was weaker), driven by 234Th adsorption onto slowly sinking particles. Model results also suggest that during bloom decays C:234Th ratios of sinking particles should be higher than expected (based on contemporaneous water column POC), because high settling velocities minimize carbon remineralization during sinking.

Evaluation of reanalysis and satellite-based sea surface winds using in situ measurements from Chinese Antarctic Expeditions

2013 ◽  
Vol 24 (3) ◽  
pp. 147
Author(s):  
Ming LI ◽  
Qinghua YANG ◽  
Jiechen ZHAO ◽  
Lin ZHANG ◽  
Chunhua LI ◽  
...  
Keyword(s):  
In Situ Measurements ◽  
Sea Surface ◽  
Surface Winds ◽  
Sea Surface Winds
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