Constructing long-term proxy series for aquatic environments with absolute dating control using a sclerochronological approach: introduction and advanced applications

2006 ◽  
Vol 57 (6) ◽  
pp. 591 ◽  
Author(s):  
Samuli Helama ◽  
Bernd R. Schöne ◽  
Bryan A. Black ◽  
Elena Dunca
Keyword(s):  
Time Series ◽  
Proxy Records ◽  
Growth Increments ◽  
Sample Series ◽  
Spline Curves ◽  
Mollusc Species ◽  
Absolute Dating ◽  
Original Time ◽  
Cross Dating

The possibility of applying absolute dating techniques to annual growth increments from the hard parts of aquatic animals was examined. This was done using the theory of cross-dating, which was adopted from dendrochronological principles. Using two mollusc species as examples, the practical issues of the method were demonstrated. Empirical data were used to evaluate the different time series analysis techniques as follows. Biological growth trends were first captured from original time series using cubic splines. Dimensionless growth indices were obtained by extracting the observed growth values from the values of spline curves as ratios. The common growth signal among the index series was quantified visually and statistically. In statistical analysis, correlations between all possible pairs of indexed sample series and, alternatively, between sample series and master chronology (the average of all other remaining time series) were calculated. It was demonstrated that sample–master correlations were consistently higher than sample–sample correlations. Sclerochronologically cross-dated time series were proved to provide absolute dating of high-resolution proxy records that assessed environmental change in marine and freshwater settings. The wider applicability of the associated techniques is discussed, and it is suggested that use of the term ‘sclerochronology’ be restricted to refer only to material or studies for which careful cross-dating has been successfully applied.

Precipitation Forecast Based on WA-SVM Theory

2012 ◽  
Vol 518-523 ◽  
pp. 4039-4042
Author(s):  
Zhen Min Zhou
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Wavelet Analysis ◽  
Precipitation Forecast ◽  
Support Vector ◽  
Estimation Model ◽  
Original Time Series ◽  
Set Up ◽  
Original Time

In order to improve the precision of medium-long term rainfall forecast, the rainfall estimation model was set up based on wavelet analysis and support vector machine (WA-SVM). It decomposed the original rainfall series to different layers through wavelet analysis, forecasted each layer by means of SVM, and finally obtained the forecast results of the original time series by composition. The model was used to estimate the monthly rainfall sequence in the watershed. Comparing with other method which only uses support vector machine(SVM), it indicates that the estimated accuracy was improved obviously.

Correlation structure of end-expiratory lung volume in anesthetized rats with intact upper airway

2000 ◽  
Vol 278 (6) ◽  
pp. R1446-R1452 ◽  
Author(s):  
Xiaobin Zhang ◽  
Eugene N. Bruce
Keyword(s):  
Time Series ◽  
Lung Volume ◽  
Hurst Exponent ◽  
Upper Airway ◽  
Correlation Structure ◽  
Original Time Series ◽  
Dispersional Analysis ◽  
Anesthetized Rats ◽  
Original Time

The correlation structure of breath-to-breath fluctuations of end-expiratory lung volume (EEV) was studied in anesthetized rats with intact airways subjected to positive and negative transrespiratory pressure (i.e., PTRP and NTRP, correspondingly). The Hurst exponent, H, was estimated from EEV fluctuations using modified dispersional analysis. We found that H for EEV was 0.5362 ± 0.0763 and 0.6403 ± 0.0561 with PTRP and NTRP, respectively (mean ± SD). Both H were significantly different from those obtained after random shuffling of the original time series. Also, H with NTRP was significantly greater than that with PTRP ( P = 0.029). We conclude that in rats breathing through the upper airway, a positive long-term correlation is present in EEV that is different between PTRP and NTRP.

scholarly journals SGD-SM: Generating Seamless Global Daily AMSR2 Soil Moisture Long-term Productions (2013–2019)

2020 ◽  
Author(s):  
Qiang Zhang ◽  
Qiangqiang Yuan ◽  
Jie Li ◽  
Yuan Wang ◽  
Fujun Sun ◽  
...  
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Satellite Orbit ◽  
Temporal Consistency ◽  
Spatial Continuity ◽  
Coverage Ratio ◽  
Original Dataset ◽  
Original Time

Abstract. High quality and long-term soil moisture productions are significant for hydrologic monitoring and agricultural management. However, the acquired daily soil moisture productions are incomplete in global land (just about 30 %∼80 % coverage ratio), due to the satellite orbit coverage and the limitations of soil moisture retrieving algorithms. To solve this inevitable problem, we develop a novel 3D spatio-temporal partial convolutional neural network (CNN) for Advanced Microwave Scanning Radiometer 2 (AMSR2) soil moisture productions gap-filling. Through the proposed framework, we generate the seamless global daily (SGD) AMSR2 soil moisture long-term productions from 2013 to 2019. To further validate the effectiveness of these productions, three verification ways are employed as follow: 1) In-situ validation; 2) Time-series validation; And 3) simulated missing regions validation. Results show that the seamless global daily soil moisture productions have reliable cooperativity with the selected in-situ values. The evaluation indexes of the reconstructed (original) dataset are R: 0.683 (0.687), RMSE: 0.099 m3/m3 (0.095 m3/m3), and MAE: 0.081 m3/m3 (0.078 m3/m3), respectively. Temporal consistency of the reconstructed daily soil moisture productions is ensured with the original time-series distribution of valid values. Besides, the spatial continuity of the reconstructed regions is also accorded with the context information (R: 0.963∼0.974, RMSE: 0.065∼0.073 m3/m3, and MAE: 0.044∼0.052 m3/m3). More details of this work are released at https://qzhang95.github.io/Projects/Global-Daily-Seamless-AMSR2/. This dataset can be downloaded at https://zenodo.org/record/3960425 (Zhang et al., 2020. DOI:https://doi.org/10.5281/zenodo.3960425).

Validation of annual growth rings in freshwater mussel shells using cross dating

2008 ◽  
Vol 65 (10) ◽  
pp. 2224-2232 ◽  
Author(s):  
Andrew L. Rypel ◽  
Wendell R. Haag ◽  
Robert H. Findlay
Keyword(s):  
Time Series ◽  
Quality Control ◽  
Freshwater Mussel ◽  
Control Technique ◽  
Measurement Time ◽  
Annual Rings ◽  
Related Variation ◽  
Age Related ◽  
Cross Dating

We examined the usefulness of dendrochronological cross-dating methods for studying long-term, interannual growth patterns in freshwater mussels, including validation of annual shell ring formation. Using 13 species from three rivers, we measured increment widths between putative annual rings on shell thin sections and then removed age-related variation by standardizing measurement time series using cubic splines. Initially, cross dating was a valuable quality control technique allowing us to correct interpretive and measurement errors in 16% of specimens. For all species, growth varied among years but was highly synchronous among individuals. Standardized measurement time series of 94% of individuals were significantly correlated with species master chronologies, and mean interseries correlations ranged from 0.37 to 0.96. Growth was also synchronous among species, even from different rivers, and growth was negatively correlated with mean annual streamflow for most species except Quadrula pustulosa from a regulated dam tailrace. Highly synchronous growth and the strong relationship to streamflow showed that large-scale environmental signals generated non-age-related variation in mussel growth giving strong support for annual formation of the growth increments we measured. Cross dating can be a valuable technique for studying freshwater mussel growth providing quality control, validation of annual rings, and reconstruction of long-term growth histories.

scholarly journals Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
R. D. García ◽  
O. E. García ◽  
E. Cuevas ◽  
V. E. Cachorro ◽  
A. Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mineral Dust ◽  
Dust Particles ◽  
Filter Radiometer ◽  
Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations  >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

scholarly journals Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

2021 ◽  
Vol 13 (2) ◽  
pp. 542
Author(s):  
Tarate Suryakant Bajirao ◽  
Pravendra Kumar ◽  
Manish Kumar ◽  
Ahmed Elbeltagi ◽  
Alban Kuriqi
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Suspended Sediment ◽  
River Basin ◽  
Time Series Data ◽  
Series Data ◽  
Discrete Wavelet ◽  
Original Time Series ◽  
Inference System ◽  
Original Time

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 416
Author(s):  
Bwalya Malama ◽  
Devin Pritchard-Peterson ◽  
John J. Jasbinsek ◽  
Christopher Surfleet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Low Permeability ◽  
Pumping Tests ◽  
Stream Flows ◽  
The Impact

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

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