scholarly journals Testing the applicability of neural networks as a gap-filling method using CH<sub>4</sub> flux data from high latitude wetlands

2013 ◽  
Vol 10 (5) ◽  
pp. 7727-7759 ◽  
Author(s):  
S. Dengel ◽  
D. Zona ◽  
T. Sachs ◽  
M. Aurela ◽  
M. Jammet ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Pearson Correlation ◽  
Time Of Day ◽  
Gap Filling ◽  
Ch4 Flux ◽  
Advantages And Disadvantages ◽  
Flux Data ◽  
Free Data ◽  
Filling Method

Abstract. Since the advancement in CH4 gas analyser technology and its applicability to eddy covariance flux measurements, monitoring of CH4 emissions is becoming more widespread. In order to accurately determine the greenhouse gas balance, high quality gap-free data is required. Currently there is still no consensus on CH4 gap-filling methods, and methods applied are still study-dependent and often carried out on low resolution daily data. In the current study, we applied artificial neural networks to six distinctively different CH4 time series from high latitudes in order to recover missing data points, explained the method and tested its functionality. We discuss the applicability of neural networks in CH4 flux studies, the advantages and disadvantages of this method, and what information we were able to extract from such models. In keeping with the principle of parsimony, we included only five standard meteorological variables traditionally measured at CH4 flux measurement sites. These included drivers such as air and soil temperature, barometric air pressure, solar radiation, and in addition wind direction (indicator of source location). Four fuzzy sets were included representing the time of day. High Pearson correlation coefficients (r) of 0.76–0.93 achieved in the final analysis are indicative for the high performance of neural networks and their applicability as a gap-filling method for CH4 flux data time series. This novel approach that we showed to be appropriate for CH4 fluxes is a step towards standardising CH4 gap-filling protocols.

scholarly journals Testing the applicability of neural networks as a gap-filling method using CH<sub>4</sub> flux data from high latitude wetlands

2013 ◽  
Vol 10 (12) ◽  
pp. 8185-8200 ◽  
Author(s):  
S. Dengel ◽  
D. Zona ◽  
T. Sachs ◽  
M. Aurela ◽  
M. Jammet ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Pearson Correlation ◽  
Time Of Day ◽  
Gap Filling ◽  
Ch4 Flux ◽  
Advantages And Disadvantages ◽  
Flux Data ◽  
Free Data ◽  
Filling Method

Abstract. Since the advancement in CH4 gas analyser technology and its applicability to eddy covariance flux measurements, monitoring of CH4 emissions is becoming more widespread. In order to accurately determine the greenhouse gas balance, high quality gap-free data is required. Currently there is still no consensus on CH4 gap-filling methods, and methods applied are still study-dependent and often carried out on low resolution, daily data. In the current study, we applied artificial neural networks to six distinctively different CH4 time series from high latitudes, explain the method and test its functionality. We discuss the applicability of neural networks in CH4 flux studies, the advantages and disadvantages of this method, and what information we were able to extract from such models. Three different approaches were tested by including drivers such as air and soil temperature, barometric air pressure, solar radiation, wind direction (indicator of source location) and in addition the lagged effect of water table depth and precipitation. In keeping with the principle of parsimony, we included up to five of these variables traditionally measured at CH4 flux measurement sites. Fuzzy sets were included representing the seasonal change and time of day. High Pearson correlation coefficients (r) of up to 0.97 achieved in the final analysis are indicative for the high performance of neural networks and their applicability as a gap-filling method for CH4 flux data time series. This novel approach which we show to be appropriate for CH4 fluxes is a step towards standardising CH4 gap-filling protocols.

scholarly journals A Comparison of Three Gap Filling Techniques for Eddy Covariance Net Carbon Fluxes in Short Vegetation Ecosystems

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaosong Zhao ◽  
Yao Huang
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Diurnal Variation ◽  
Eddy Covariance ◽  
Nonlinear Regression ◽  
Missing Values ◽  
The Other ◽  
Gap Filling ◽  
Benchmark Datasets ◽  
Filling Method

Missing data is an inevitable problem when measuring CO2, water, and energy fluxes between biosphere and atmosphere by eddy covariance systems. To find the optimum gap-filling method for short vegetations, we review three-methods mean diurnal variation (MDV), look-up tables (LUT), and nonlinear regression (NLR) for estimating missing values of net ecosystem CO2exchange (NEE) in eddy covariance time series and evaluate their performance for different artificial gap scenarios based on benchmark datasets from marsh and cropland sites in China. The cumulative errors for three methods have no consistent bias trends, which ranged between −30 and +30 mgCO2 m−2from May to October at three sites. To reduce sum bias in maximum, combined gap-filling methods were selected for short vegetation. The NLR or LUT method was selected after plant rapidly increasing in spring and before the end of plant growing, and MDV method was used to the other stage. The sum relative error (SRE) of optimum method ranged between −2 and +4% for four-gap level at three sites, except for 55% gaps at soybean site, which also obviously reduced standard deviation of error.

scholarly journals Efficacy of Feedforward and LSTM Neural Networks at Predicting and Gap Filling Coastal Ocean Timeseries: Oxygen, Nutrients, and Temperature

2021 ◽  
Vol 8 ◽  
Author(s):  
Steefan Contractor ◽  
Moninya Roughan
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Short Term Memory ◽  
Data Sets ◽  
Gap Filling ◽  
Diverse Range ◽  
Big Data Applications ◽  
Free Data ◽  
Before And After ◽  
Ocean Environment

Ocean data timeseries are vital for a diverse range of stakeholders (ranging from government, to industry, to academia) to underpin research, support decision making, and identify environmental change. However, continuous monitoring and observation of ocean variables is difficult and expensive. Moreover, since oceans are vast, observations are typically sparse in spatial and temporal resolution. In addition, the hostile ocean environment creates challenges for collecting and maintaining data sets, such as instrument malfunctions and servicing, often resulting in temporal gaps of varying lengths. Neural networks (NN) have proven effective in many diverse big data applications, but few oceanographic applications have been tested using modern frameworks and architectures. Therefore, here we demonstrate a “proof of concept” neural network application using a popular “off-the-shelf” framework called “TensorFlow” to predict subsurface ocean variables including dissolved oxygen and nutrient (nitrate, phosphate, and silicate) concentrations, and temperature timeseries and show how these models can be used successfully for gap filling data products. We achieved a final prediction accuracy of over 96% for oxygen and temperature, and mean squared errors (MSE) of 2.63, 0.0099, and 0.78, for nitrates, phosphates, and silicates, respectively. The temperature gap-filling was done with an innovative contextual Long Short-Term Memory (LSTM) NN that uses data before and after the gap as separate feature variables. We also demonstrate the application of a novel dropout based approach to approximate the Bayesian uncertainty of these temperature predictions. This Bayesian uncertainty is represented in the form of 100 monte carlo dropout estimates of the two longest gaps in the temperature timeseries from a model with 25% dropout in the input and recurrent LSTM connections. Throughout the study, we present the NN training process including the tuning of the large number of NN hyperparameters which could pose as a barrier to uptake among researchers and other oceanographic data users. Our models can be scaled up and applied operationally to provide consistent, gap-free data to all data users, thus encouraging data uptake for data-based decision making.

Prediction of corrections for the Polish time scale UTC(PL) using artificial neural networks

2013 ◽  
Vol 61 (3) ◽  
pp. 589-594 ◽  
Author(s):  
M. Luzar ◽  
Ł. Sobolewski ◽  
W. Miczulski ◽  
J. Korbicz
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Time Scale ◽  
Theoretical Background ◽  
Group Method ◽  
Prediction Errors ◽  
Advantages And Disadvantages ◽  
Different Types ◽  
Artificial Neural

Abstract In this paper, the effectiveness of using Artificial Neural Networks (ANNs) for predicting the corrections of the Polish time scale UTC(PL) (Universal Coordinated Time) is presented. In particular, prediction results for the different types of neural networks, i.e., the MLP (MultiLayer Perceprton), the RBF (Radial Basis Function) and the GMDH (Group Method of Data Handling) are shown. The main advantages and disadvantages of using such types of neural networks are discussed. The prediction of corrections is performed using two methods: the time series analysis method and the regression method. The input data were prepared suitable for the above mentioned methods, based on two time series, ts1 and ts2. The designation of prediction errors for specified days and the influence of data quantity for the prediction error are considered. The paper consists of five sections. After Introduction, in Sec. 2, the theoretical background for different types of neural networks is presented. Section 3 shows data preparation for the appropriate type of neural network. The experimental results are presented in Sec. 4. Finally, Sec. 5 concludes the paper.

scholarly journals Using Deep Learning to Fill Spatio-Temporal Data Gaps in Hydrological Monitoring Networks

2019 ◽  
Author(s):  
Huiying Ren ◽  
Erol Cromwell ◽  
Ben Kravitz ◽  
Xingyuan Chen
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Series Data ◽  
Percentage Error ◽  
Gap Filling ◽  
Error Statistics ◽  
Groundwater Aquifer ◽  
Data Gaps ◽  
Filling Method ◽  
Spatio Temporal

Abstract. Long-term spatio-temporal changes in subsurface hydrological flow are usually quantified through a network of wells; however, such observations often are spatially sparse and temporal gaps exist due to poor quality or instrument failure. In this study, we explore the ability of deep neural networks to fill in gaps in spatially distributed time-series data. We selected a location at the U.S. Department of Energy's Hanford site to demonstrate and evaluate the new method, using a 10-year spatio-temporal hydrological dataset of temperature, specific conductance, and groundwater table elevation from 42 wells that monitor the dynamic and heterogeneous hydrologic exchanges between the Columbia River and its adjacent groundwater aquifer. We employ a long short-term memory (LSTM)-based architecture, which is specially designed to address both spatial and temporal variations in the property fields. The performance of gap filling using an LSTM framework is evaluated using test datasets with synthetic data gaps created by assuming the observations were missing for a given time window (i.e., gap length), such that the mean absolute percentage error can be calculated against true observations. Such test datasets also allow us to examine how well the original nonlinear dynamics are captured in gap-filled time series beyond the error statistics. The performance of the LSTM-based gap-filling method is compared to that of a traditional, popular gap-filling method: autoregressive integrated moving average (ARIMA). Although ARIMA appears to perform slightly better than LSTM on average error statistics, LSTM is better able to capture nonlinear dynamics that are present in time series. Thus, LSTMs show promising potential to outperform ARIMA for gap filling in highly dynamic time-series observations characterized by multiple dominant modes of variability. Capturing such dynamics is essential to generate the most valuable observations to advance our understanding of dynamic complex systems.

scholarly journals General methods of forecasting nonlinear nonstationary processes based on mathematical models using statistical data

2021 ◽  
pp. 79-86
Author(s):  
Oleg Belas ◽  
Andrii Belas
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Mathematical Models ◽  
Recurrent Neural Networks ◽  
Statistical Data ◽  
Economic Systems ◽  
Nonstationary Processes ◽  
General Technique ◽  
New Approach ◽  
Advantages And Disadvantages

The article considers the problem of forecasting nonlinear nonstationary processes, presented in the form of time series, which can describe the dynamics of processes in both technical and economic systems. The general technique of analysis of such data and construction of corresponding mathematical models based on autoregressive models and recurrent neural networks is described in detail. The technique is applied on practical examples while performing the comparative analysis of models of forecasting of quantity of channels of service of cellular subscribers for a given station and revealing advantages and disadvantages of each method. The need to improve the existing methodology and develop a new approach is formulated.

scholarly journals An empirical survey of data augmentation for time series classification with neural networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254841
Author(s):  
Brian Kenji Iwana ◽  
Seiichi Uchida
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Data Augmentation ◽  
Time Series Data ◽  
Decomposition Methods ◽  
Generative Models ◽  
Series Data ◽  
Time Series Classification ◽  
Empirical Survey ◽  
Advantages And Disadvantages

In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase generalization. However, in the field of time series recognition, many datasets are often very small. One method of addressing this problem is through the use of data augmentation. In this paper, we survey data augmentation techniques for time series and their application to time series classification with neural networks. We propose a taxonomy and outline the four families in time series data augmentation, including transformation-based methods, pattern mixing, generative models, and decomposition methods. Furthermore, we empirically evaluate 12 time series data augmentation methods on 128 time series classification datasets with six different types of neural networks. Through the results, we are able to analyze the characteristics, advantages and disadvantages, and recommendations of each data augmentation method. This survey aims to help in the selection of time series data augmentation for neural network applications.

scholarly journals Towards a More General Method for Filling Gaps in Time Series

2011 ◽  
Vol 7 (S285) ◽  
pp. 392-393
Author(s):  
J. Pascual-Granado ◽  
R. Garrido ◽  
J. Gutirrez-Soto ◽  
S. Martín-Ruiz
Keyword(s):  
Time Series ◽  
Interpolation Method ◽  
Moving Average ◽  
Light Curves ◽  
Arma Models ◽  
Gap Filling ◽  
South Atlantic Anomaly ◽  
Filling Method ◽  
Auto Regressive ◽  
General Method

AbstractThe need for a proper interpolation method for data coming from space missions like CoRoT is emphasized. A new gap-filling method is introduced which is based on auto-regressive moving average interpolation (ARMA) models. The method is tested on light curves from stars observed by the CoRoT satellite, filling the gaps caused by the South Atlantic Anomaly.

Gap-filling continuously-measured soil respiration data with time-series-based methods

2020 ◽  
Author(s):  
Junbin Zhao ◽  
Holger Lange ◽  
Helge Meissner
Keyword(s):  
Time Series ◽  
Soil Respiration ◽  
Boreal Forests ◽  
Singular Spectrum Analysis ◽  
R Package ◽  
Similar Degree ◽  
Gap Filling ◽  
Flux Measurements ◽  
Filling Method ◽  
Established Relationship

&lt;p&gt;Soil respiration is an important ecosystem process that releases carbon dioxide into the atmosphere. While soil respiration can be measured continuously at high temporal resolutions, gaps in the dataset are inevitable, leading to uncertainties in carbon budget estimations. Therefore, robust methods used to fill the gaps are needed. The process-based non-linear least squares (NLS) regression is the most widely used gap-filling method, which utilizes the established relationship between the soil respiration and temperature. In addition to NLS, we also implemented three other methods based on: 1) artificial neural networks (ANN), driven by temperature and moisture measurements, 2) singular spectrum analysis (SSA), relying only on the time series itself, and 3) the expectation-maximization (EM) approach, referencing to parallel flux measurements in the spatial vicinity. Six soil respiration datasets (2017-2019) from two boreal forests were used for benchmarking. Artificial gaps were randomly introduced into the datasets and then filled using the four methods. The time-series-based methods, SSA and EM, showed higher accuracies than NLS and ANN in small gaps (&lt;1 day). In larger gaps (15 days), the performance was similar among NLS, SSA and EM; however, ANN showed large errors in gaps that coincided with precipitation events. Compared to the observations, gap-filled data by SSA showed similar degree of variances and those filled by EM were associated with similar first-order autocorrelation coefficients. In contrast, data filled by both NLS and ANN exhibited lower variance and higher autocorrelation than the observations. For estimations of the annual soil respiration budget, NLS, SSA and EM produced satisfying results with budget errors &lt; 6% while ANN exhibited larger errors up to 16.0%. Our study highlights the two time-series-based methods which showed great potential in gap-filling carbon flux data, especially when other environmental variables are unavailable. The R code to perform the gap-filling with the four methods in this study is incorporated into the R package &amp;#8220;FluxGapsR&amp;#8221; freely available at https://github.com/junbinzhao/FluxGapsR/.&lt;/p&gt;

scholarly journals Spatial and Temporal Adaptive Gap-Filling Method Producing Daily Cloud-Free NDSI Time Series

2020 ◽  
Vol 13 ◽  
pp. 2251-2263 ◽  
Author(s):  
Siyong Chen ◽  
Xiaoyan Wang ◽  
Hui Guo ◽  
Peiyao Xie ◽  
Abuobaida M. Sirelkhatim
Keyword(s):  
Time Series ◽  
Gap Filling ◽  
Filling Method
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