scholarly journals Mapping Flood Extent and Frequency from Sentinel-1 Imagery during the Extremely Warm Winter of 2020 in Boreal Floodplains and Forests

2021 ◽  
Vol 13 (23) ◽  
pp. 4949
Author(s):  
Liis Sipelgas ◽  
Age Aavaste ◽  
Rivo Uiboupin
Keyword(s):  
Water Level ◽  
Time Series Data ◽  
Risk Mitigation ◽  
Open Water ◽  
Water Flooding ◽  
Series Data ◽  
Processing Scheme ◽  
Mild Winter ◽  
Gauge Data ◽  
Set Up

The current study presents a methodology for water mapping from Sentinel-1 (S1) data and a flood extent analysis of the three largest floodplains in Estonia. The automatic processing scheme of S1 data was set up for the mapping of open-water flooding (OWF) and flooding under vegetation (FUV). The extremely mild winter of 2019/2020 resulted in several large floods at floodplains that were detected from S1 imagery with a maximal OWF extent up to 5000 ha and maximal FUV extent up to 4500 ha. A significant correlation (r2 > 0.6) between the OWF extent and the closest gauge data was obtained for inland riverbank floodplains. The outcome enabled us to define the water level at which the water exceeds the shoreline and flooding starts. However, for a coastal river delta floodplain, a lower correlation (r2 < 0.34) with gauge data was obtained, and the excess of river coastline could not be related to a certain water level. At inland riverbank floodplains, the extent of FUV was three times larger compared to that of OWF. The correlation between the water level and FUV was <0.51, indicating that the river water level at these test sites can be used as a proxy for forest floods. Relating conventional gauge data to S1 time series data contributes to flood risk mitigation.

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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.

Implementing Business Intelligence in Electricity Markets

2010 ◽  
pp. 283-295
Author(s):  
José Ramón Cancelo ◽  
Antoni Espasa
Keyword(s):  
Electricity Markets ◽  
Business Intelligence ◽  
Explicit Knowledge ◽  
Time Series Data ◽  
Contextual Information ◽  
Electricity Consumption ◽  
Point Of View ◽  
Series Data ◽  
Basic Ideas ◽  
Set Up

The authors elaborate on three basic ideas that should guide the implementation of business intelligence tools. First, the authors advocate for closing the gap between structured information and contextual information. Second, they emphasize the need for adopting the point of view of the organization to assess the relevance of any proposal. In the third place, they remark that any new tool is expected to become a relevant instrument to enhance the learning of the organization and to generate explicit knowledge. To illustrate their point, they discuss how to set up a forecasting support system to predict electricity consumption that converts raw time series data into market intelligence, to meet the needs of a major organization operating at the Spanish electricity markets.

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 A Review of Post Privatization Performance of Ashaka Cement Manufacturing Company Nigeria PLC through the use of Financial Ratios

2012 ◽  
Vol 3 (12) ◽  
pp. 382-388
Author(s):  
Abubakar Muhammed Magaji
Keyword(s):  
Capital Investment ◽  
Time Series Data ◽  
Technical Committee ◽  
Series Data ◽  
Company Performance ◽  
Public Enterprises ◽  
Formal Sector ◽  
Set Up ◽  
Cement Manufacturing ◽  
Fixed Capital Investment

Privatization as a reform policy package has been adopted by both developed and developing countries’ economies. Nigeria as a developing country has large public enterprises which has about 57 percent of fixed capital investment and about 66 percent of formal sector employment by 1997. These enterprises performed below expectation due to multiple problems. Technical Committee on Privatization and Commercialization (TCPC) was set up to privatize the enterprises and the privatization have since commenced. The paper reviewed Ashaka cement company performance as a privatized enterprise after privatization. Managers of business organization must have reliable analytical tools for taking a rational decision. Ratio is one of such tools. Time series data from Ashaka Cement Company was used. The performance of the company has improved after privatization.

scholarly journals Smart Helmet: Thresh-Learner–Online Machine Learning on Data Streams

2019 ◽  
Vol 9 (1S3) ◽  
pp. 466-473
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Real Life ◽  
General Purpose ◽  
Streaming Data ◽  
Series Data ◽  
Dynamic Threshold ◽  
Wide Range ◽  
Set Up

Today, with an enormous generation and availability of time series data and streaming data, there is an increasing need for an automatic analyzing architecture to get fast interpretations and results. One of the significant potentiality of streaming analytics is to train and model each stream with unsupervised Machine Learning (ML) algorithms to detect anomalous behaviors, fuzzy patterns, and accidents in real-time. If executed reliably, each anomaly detection can be highly valuable for the application. In this paper, we propose a dynamic threshold setting system denoted as Thresh-Learner, mainly for the Internet of Things (IoT) applications that require anomaly detection. The proposed model enables a wide range of real-life applications where there is a necessity to set up a dynamic threshold over the streaming data to avoid anomalies, accidents or sending alerts to distant monitoring stations. We took the major problem of anomalies and accidents in coal mines due to coal fires and explosions. This results in loss of life due to the lack of automated alarming systems. We propose Thresh-Learner, a general purpose implementation for setting dynamic thresholds. We illustrate it through the Smart Helmet for coal mine workers which seamlessly integrates monitoring, analyzing and dynamic thresholds using IoT and analysis on the cloud.

scholarly journals Options for calibrating CERES-maize genotype specific parameters under data-scarce environments

2018 ◽  
Author(s):  
A.A Adnan ◽  
J. Diels ◽  
J.M. Jibrin ◽  
A.Y. Kamara ◽  
P. Craufurd ◽  
...  
Keyword(s):  
Field Experiments ◽  
Time Series Data ◽  
Simulation Models ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Planting Dates ◽  
Maize Varieties ◽  
Using Data ◽  
Set Up ◽  
Nitrogen Contents

AbstractMost crop simulation models require the use of Genotype Specific Parameters (GSPs) which provide the Genotype component of G×E×M interactions. Estimation of GSPs is the most difficult aspect of most modelling exercises because it requires expensive and time-consuming field experiments. GSPs could also be estimated using multi-year and multi locational data from breeder evaluation experiments. This research was set up with the following objectives: i) to determine GSPs of 10 newly released maize varieties for the Nigerian Savannas using data from both calibration experiments and by using existing data from breeder varietal evaluation trials; ii) to compare the accuracy of the GSPs generated using experimental and breeder data; and iii) to evaluate CERES-Maize model to simulate grain and tissue nitrogen contents. For experimental evaluation, 8 different experiments were conducted during the rainy and dry seasons of 2016 across the Nigerian Savanna. Breeder evaluation data was also collected for 2 years and 7 locations. The calibrated GSPs were evaluated using data from a 4 year experiment conducted under varying nitrogen rates (0, 60 and 120kg N ha−1). For the model calibration using experimental data, calculated model efficiency (EF) values ranged between 0.86-0.92 and coefficient of determination (d-index) between 0.92-0.98. Calibration of time-series data produced nRMSE below 7% while all prediction deviations were below 10% of the mean. For breeder experiments, EF (0.52-0.81) and d-index (0.46-0.83) ranges were lower. Prediction deviations were below 17% of the means for all measured variables. Model evaluation using both experimental and breeder trials resulted in good agreement (low RMSE, high EF and d-index values) between observed and simulated grain yields, and tissue and grain nitrogen contents. We conclude that higher calibration accuracy of CERES-Maize model is achieved from detailed experiments. If unavailable, data from breeder experimental trials collected from many locations and planting dates can be used with lower but acceptable accuracy.

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.

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.

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