scholarly journals Timeline Visualization Uncovers Gaps in Archived Tsunami Water Level Data

2021 ◽  
Vol 3 ◽  
Author(s):  
Aaron D. Sweeney
Keyword(s):  
Time Series ◽  
Water Level ◽  
Time Series Data ◽  
Deep Ocean ◽  
Rapid Identification ◽  
Series Data ◽  
Level Data ◽  
Water Level Data ◽  
Data Gaps ◽  
Coastal Water Level

We demonstrate that data abstraction via a timeline visualization is highly effective at allowing one to discover patterns in the underlying data. We describe the rapid identification of data gaps in the archival time-series records of deep-ocean pressure and coastal water level observations collected to support the NOAA Tsunami Program and successful measures taken to rescue these data. These data gaps had persisted for years prior to the development of timeline visualizations to represent when data were collected. This approach can be easily extended to all types of time-series data and the author recommends this type of temporal visualization become a routine part of data management, whether one collects data or archives data.

Chaotic Identification of Hourly and Daily Water Level Time Series Data in Different Areas of Elevation at Pahang River

2021 ◽  
Vol 12 (3) ◽  
pp. 2427-2435
Author(s):  
Adib Mashuri Et.al
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Water Level ◽  
Time Scale ◽  
Time Series Data ◽  
Phase Space Reconstruction ◽  
Series Data ◽  
Level Data ◽  
Water Level Data ◽  
Daily Time

This study focused on chaotic analysis of water level data in different elevations located in the highland and lowland areas. This research was conducted considering the uncertain water level caused by the river flow from highland to lowland areas. The analysis was conducted using the data collected from the four area stations along Pahang River on different time scales which were hourly and daily time series data. The resulted findings were relevant to be used by the local authorities in water resource management in these areas. Two methods were used for the analysis process which included Cao method and phase space plot. Both methods are based on phase space reconstruction that is referring to reconstruction of one dimensional data (water level data) to d-dimensional phase space in order to determine the dynamics of the system. The combination of parameters  and d is required in phase space reconstruction. Results showed that (i) the combination of phase space reconstruction’s parameters gave a higher value of parameters by using hourly time scale compared to daily time scale for different elevation; (ii) different elevation gave impact on the values of phase space reconstructions’ parameters; (iii) chaotic dynamics existed using Cao method and phase space plot for different elevation and time scale. Hence, water level data with different time scale from different elevation in Pahang River can be used in the development of prediction model based on chaos approach.

Products and Services Available from U.S. NOAA NCEI Archive of Water Level Data

2021 ◽  
Author(s):  
Aaron Sweeney ◽  
George Mungov ◽  
Lindsey Wright
Keyword(s):  
Time Series ◽  
Water Level ◽  
Time Series Data ◽  
Tide Gauge ◽  
Series Data ◽  
Ocean Bottom ◽  
Bottom Pressure ◽  
Pressure Data ◽  
Level Data ◽  
Water Level Data

<p>The U.S. National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) archives analog and digital coastal water level data and ocean-bottom pressure data, digitizes select analog data, and performs quality-control and tidal analysis of these data.  The analog tide gauge records (marigrams) cover selected tsunami events between 1854 and 1981 observed at stations across the globe.  There are 3,486 high-resolution scanned marigrams in the archive.  The digital tide gauge data, primarily U.S. stations, have been collected at 1-minute sampling since 2008.  The ocean-bottom pressure data have been collected since 1983.  These time-series data are complementary to the maximum wave heights recorded in the NCEI/World Data Service Global Historical Tsunami Database.  With the introduction of visual timeline inventories, our NOAA partners have helped us identify, recover, and backfill gaps in our archive.  All water level data and products are converted to standardized file formats to reduce barriers to re-use. We provide quality-controlled water level data, computed astronomical tides, details on the harmonic tidal analysis results, and spectra to assess the quality of the de-tiding. Researchers use the quality-controlled data to validate tsunami propagation and storm surge models.  Select scanned marigram images are digitized into numerical time-series data by hand-selecting data points along the inked tidal curves. Though automated data point selection capabilities exist, when tested, they did not accurately detect faint traces and consistently failed to correctly select the peak and trough values. Hand-selection ensured that the maximum and minimum values important across water level research would be accurately recorded. From 2016 to 2019, we have digitized 48 of these images, across ten tsunami events, into ready-to-use, digital time-series data.  In the event of a tsunami, we augment our holdings by collecting and processing data from the National Hydrographic Services in the affected regions and from the United Nations Education, Scientific and Cultural Organization Intergovernmental Oceanographic Commission (UNESCO IOC) Sea Level Stations Monitoring Facility. Currently, UNESCO IOC does not process these data. These data products are then made available via Tsunami Event Pages. </p>

scholarly journals CNN-BiLSTM water level prediction method with attention mechanism

2021 ◽  
Vol 2078 (1) ◽  
pp. 012032
Author(s):  
Qingqing Nie ◽  
Dingsheng Wan ◽  
Rui Wang
Keyword(s):  
Time Series ◽  
Water Level ◽  
Time Series Data ◽  
Prediction Method ◽  
Attention Mechanism ◽  
Series Data ◽  
Single Model ◽  
Level Data ◽  
Water Level Data ◽  
Water Level Prediction

Abstract Hydrological time series data is stochastic and complex, and the importance of its historical features is different. A single model is difficult to overcome its own limitations when dealing with hydrological time series prediction problems, and the prediction accuracy of a single model can be further improved. According to the characteristics of hydrological time series data, a CNN-BiLSTM water level prediction method with attention mechanism is proposed. In this paper, CNN extracts the spatial characteristics of water level data and BiLSTM learns the time period characteristics by combining the past and future sequence information, attention mechanism is introduced to focus the salient features in the sequence. Taking the hourly water level data of Pinghe basin in China as experimental basis, experimental result shows that this method is more accuracy than Support Vector Machine (SVM), Temporal Convolutional Neural network (TCN), and Bidirectional Long Short-Term Memory network (BiLSTM) model.

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.

Prediction of Water Level Using Time Series, Wavelet and Neural Network Approaches

2022 ◽  
pp. 1077-1097
Author(s):  
Nguyen Quang Dat ◽  
Ngoc Anh Nguyen Thi ◽  
Vijender Kumar Solanki ◽  
Ngo Le An
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Water Level ◽  
Project Work ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Input Variables ◽  
Network Approaches ◽  
Short Time

To control water resources in many domains such as agriculture, flood forecasting, and hydro-electrical dams, forecasting water level needs to predict. In this article, a new computational approach using a data driven model and time series is proposed to calculate the forecast water level in short time. Concretely, wavelet-artificial neural network (WAANN) and time series (TS) are combined together called WAANN-TS that encourages the advantage of each model. For this real time project work, Yen Bai station, Northwest Vietnam was chosen as an experimental case study to apply the proposed model. Input variables into the Wavelet-ANN structure is water level data. Time series and ANN models are built, and their performances are compared. The results indicate the greater accuracy of the proposed models at Hanoi station. The final proposal WAANN−TS for water level forecasting shows good performance with root mean square error (RMSE) from 10−10 to 10−11.

scholarly journals Time-series analysis of the risk factors for haemorrhagic fever with renal syndrome: comparison of statistical models

2006 ◽  
Vol 135 (2) ◽  
pp. 245-252 ◽  
Author(s):  
W. HU ◽  
K. MENGERSEN ◽  
P. BI ◽  
S. TONG
Keyword(s):  
Time Series ◽  
Water Level ◽  
Crop Production ◽  
Time Series Data ◽  
Linear Time ◽  
Additive Model ◽  
Series Data ◽  
Haemorrhagic Fever ◽  
Independent Variables ◽  
China Model

Three conventional regression models were compared using the time-series data of the occurrence of haemorrhagic fever with renal syndrome (HFRS) and several key climatic and occupational variables collected in low-lying land, Anhui Province, China. Model I was a linear time series with normally distributed residuals; model II was a generalized linear model with Poisson-distributed residuals and a log link; and model III was a generalized additive model with the same distributional features as model II. Model I was fitted using least squares whereas models II and III were fitted using maximum likelihood. The results show that the correlations between the HFRS incidence and the independent variables measured (i.e. difference in water level, autumn crop production and density of Apodemus agrarius) ranged from −0·40 to 0·89. The HFRS incidence was positively associated with density of A. agrarius and crop production, but was inversely associated with difference in water level. The residual analyses and the examination of the accuracy of the models indicate that model III may be the most suitable in the assessment of the relationship between the incidence of HFRS and the independent variables.

scholarly journals Stochastic modeling of Lake Van water level time series with jumps and multiple trends

2013 ◽  
Vol 10 (2) ◽  
pp. 2353-2371 ◽  
Author(s):  
H. Aksoy ◽  
N. E. Unal ◽  
E. Eris ◽  
M. I. Yuce
Keyword(s):  
Time Series ◽  
Water Level ◽  
Eastern Anatolia ◽  
Lake Van ◽  
Data Set ◽  
Lake Water Level ◽  
Trend Model ◽  
Level Data ◽  
Water Level Data ◽  
Surrounding Areas

Abstract. In 1990s, water level in the closed-basin Lake Van located in the Eastern Anatolia, Turkey has risen up about 2 m. Analysis of the hydrometeorological shows that change in the water level is related to the water budget of the lake. In this study, a stochastic model is generated using the measured monthly water level data of the lake. The model is derived after removal of trend and periodicity in the data set. Trend observed in the lake water level time series is fitted by mono- and multiple-trend lines. For the multiple-trend, the time series is first divided into homogeneous segments by means of SEGMENTER, segmentation software. Four segments are found meaningful practically each fitted with a trend line. Two models considering mono- and multiple-trend time series are developed. The multiple-trend model is found better for planning future development in surrounding areas of the lake.

scholarly journals Advanced Hybrid Machine Learning Algorithms for Multistep Lake Water Level Forecasting

2021 ◽  
Author(s):  
Elham Fijani ◽  
Khabat Khosravi ◽  
Rahim Barzegar ◽  
John Quilty ◽  
Jan Adamowski ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Water Level ◽  
Lake Michigan ◽  
Model Building ◽  
Time Series Data ◽  
Machine Learning Algorithms ◽  
Series Data ◽  
Lake Water Level ◽  
Visually Based

Abstract Random Tree (RT) and Iterative Classifier Optimizer (ICO) based on Alternating Model Tree (AMT) regressor machine learning (ML) algorithms coupled with Bagging (BA) or Additive Regression (AR) hybrid algorithms were applied to forecasting multistep ahead (up to three months) Lake Superior and Lake Michigan water level (WL). Partial autocorrelation (PACF) of each lake’s WL time series estimated the most important lag times — up to five months in both lakes — as potential inputs. The WL time series data was partitioned into training (from 1918 to 1988) and testing (from 1989 to 2018) for model building and evaluation, respectively. Developed algorithms were validated through statistically and visually based metric using testing data. Although both hybrid ensemble algorithms improved individual ML algorithms’ performance, the BA algorithm outperformed the AR algorithm. As a novel model in forecasting problems, the ICO algorithm was shown to have great potential in generating robust multistep lake WL forecasts.

scholarly journals Error in hydraulic head and gradient time-series measurements: a quantitative appraisal

2019 ◽  
Vol 23 (9) ◽  
pp. 3603-3629 ◽  
Author(s):  
Gabriel C. Rau ◽  
Vincent E. A. Post ◽  
Margaret Shanafield ◽  
Torsten Krekeler ◽  
Eddie W. Banks ◽  
...  
Keyword(s):  
Time Series ◽  
Water Level ◽  
Measurement Errors ◽  
Time Series Data ◽  
Measurement Techniques ◽  
Hydraulic Head ◽  
Series Data ◽  
Sufficient Information ◽  
Regional Flow ◽  
Starting Point

Abstract. Hydraulic head and gradient measurements underpin practically all investigations in hydrogeology. There is sufficient information in the literature to suggest that head measurement errors can impede the reliable detection of flow directions and significantly increase the uncertainty of groundwater flow rate calculations. Yet educational textbooks contain limited content regarding measurement techniques, and studies rarely report on measurement errors. The objective of our study is to review currently accepted standard operating procedures in hydrological research and to determine the smallest head gradients that can be resolved. To this aim, we first systematically investigate the systematic and random measurement errors involved in collecting time-series information on hydraulic head at a given location: (1) geospatial position, (2) point of head, (3) depth to water, and (4) water level time series. Then, by propagating the random errors, we find that with current standard practice, horizontal head gradients <10-4 are resolvable at distances ⪆170 m. Further, it takes extraordinary effort to measure hydraulic head gradients <10-3 over distances <10 m. In reality, accuracy will be worse than our theoretical estimates because of the many possible systematic errors. Regional flow on a scale of kilometres or more can be inferred with current best-practice methods, but processes such as vertical flow within an aquifer cannot be determined until more accurate and precise measurement methods are developed. Finally, we offer a concise set of recommendations for water level, hydraulic head and gradient time-series measurements. We anticipate that our work contributes to progressing the quality of head time-series data in the hydrogeological sciences and provides a starting point for the development of universal measurement protocols for water level data collection.

scholarly journals Hydrodynamic Characterization of Mugla Karst Aquifer Using Correlation and Spectral Analyses on the Rainfall and Springs Water-Level Time Series

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 85
Author(s):  
Çağdaş Sağır ◽  
Bedri Kurtuluş ◽  
Moumtaz Razack
Keyword(s):  
Time Series ◽  
Water Level ◽  
Time Series Data ◽  
Karst Aquifer ◽  
Series Data ◽  
Karst Aquifers ◽  
Wet Season ◽  
Spectral Analyses ◽  
Aquifer System

Karst aquifers have been an important research topic for hydrologists for years. Due to their high storage capacity, karst aquifers are an important source of water for the environment. On the other hand, it is safety-critical because of its role in floods. Mugla Karst Aquifer (SW, Turkey) is the only major water-bearing formation in the close environs of Mugla city. Flooding in the wet season occurs every year in the recharge plains. The aquifer discharges by the seaside springs in the Akyaka district which is the main touristic point of interest in the area. Non-porous irregular internal structures make the karsts more difficult to study. Therefore, many different methodologies have been developed over the years. In this study, unit hydrograph analysis, correlation and spectral analyses were applied on the rainfall and spring water-level time series data. Although advanced karst formations can be seen on the surface like the sinkholes, it has been revealed that the interior structure is not highly karstified. 100–130 days of regulation time was found. This shows that the Mugla Karst has quite inertial behavior. Yet, the storage of the aquifer system is quite high, and the late infiltration effect caused by alluvium plains was detected. This characterization of the hydrodynamic properties of the Mugla karst system represents an important step to consider the rational exploitation of its water resources in the near future.

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