scholarly journals Managing and Utilizing Big Data in Atmospheric Monitoring Systems for Underground Coal Mines

2021 ◽  
Vol 5 (1) ◽  
pp. 78
Author(s):  
Juan Diaz ◽  
Zach Agioutantis ◽  
Dionissios T. Hristopulos ◽  
Steven Schafrik
Keyword(s):  
Time Series ◽  
Real Time ◽  
Monitoring And Evaluation ◽  
Barometric Pressure ◽  
Nonlinear Dependence ◽  
Sensor Calibration ◽  
Monitoring Systems ◽  
Transient Effects ◽  
Time Step ◽  
Atmospheric Monitoring

Underground coal mining Atmospheric Monitoring Systems (AMS) have been implemented for real-time or near real-time monitoring and evaluation of the mine atmosphere and related parameters such as gas concentration (e.g., CH4, CO, O2), fan performance (e.g., power, speed), barometric pressure, ambient temperature, humidity, etc. Depending on the sampling frequency, AMS can collect and manage a tremendous amount of data, which mine operators typically consult for everyday operations as well as long-term planning and more effective management of ventilation systems. The raw data collected by AMS need considerable pre-processing and filtering before they can be used for analysis. This paper discusses different challenges related to filtering raw AMS data in order to identify and remove values due to sensor breakdowns, sensor calibration periods, transient values due to operational considerations, etc., as well as to homogenize time series for different variables. The statistical challenges involve the removal of faulty values and outliers (due to systematic problems) and transient effects, gap-filling (by means of interpolation methods), and homogenization (setting a common time reference and time step) of the respective time series. The objective is to derive representative and synchronous time series values that can subsequently be used to estimate summary statistics of AMS and to infer correlations or nonlinear dependence between different data streams. Identification and modeling of statistical dependencies can be further exploited to develop predictive equations based on time series models.

From mess to mass: a methodology for calculating storm event pollutant loads with their uncertainties, from continuous raw data time series

2011 ◽  
Vol 63 (3) ◽  
pp. 369-376 ◽  
Author(s):  
M. Métadier ◽  
J. -L. Bertrand-Krajewski
Keyword(s):  
Time Series ◽  
Oxygen Demand ◽  
Software Tool ◽  
Large Data ◽  
Sensor Calibration ◽  
Storm Event ◽  
Storm Events ◽  
Time Step ◽  
Raw Data ◽  
Sewer Systems

With the increasing implementation of continuous monitoring of both discharge and water quality in sewer systems, large data bases are now available. In order to manage large amounts of data and calculate various variables and indicators of interest it is necessary to apply automated methods for data processing. This paper deals with the processing of short time step turbidity time series to estimate TSS (Total Suspended Solids) and COD (Chemical Oxygen Demand) event loads in sewer systems during storm events and their associated uncertainties. The following steps are described: (i) sensor calibration, (ii) estimation of data uncertainties, (iii) correction of raw data, (iv) data pre-validation tests, (v) final validation, and (vi) calculation of TSS and COD event loads and estimation of their uncertainties. These steps have been implemented in an integrated software tool. Examples of results are given for a set of 33 storm events monitored in a stormwater separate sewer system.

Model of multicomponent narrow-band periodical non-stationary random signal

2020 ◽  
Vol 2020 (48) ◽  
pp. 17-24
Author(s):  
I.M. Javorskyj ◽  
◽  
R.M. Yuzefovych ◽  
P.R. Kurapov ◽  
◽  
...  
Keyword(s):  
Time Series ◽  
Real Time ◽  
Hilbert Transform ◽  
Spectral Properties ◽  
Narrow Band ◽  
Analytic Signal ◽  
Random Signal ◽  
Hilbert Transformation ◽  
The Hilbert Transform

The correlation and spectral properties of a multicomponent narrowband periodical non-stationary random signal (PNRS) and its Hilbert transformation are considered. It is shown that multicomponent narrowband PNRS differ from the monocomponent signal. This difference is caused by correlation of the quadratures for the different carrier harmonics. Such features of the analytic signal must be taken into account when we use the Hilbert transform for the analysis of real time series.

Potential Urban Rainfall Prediction Measurement System

1984 ◽  
Vol 16 (8-9) ◽  
pp. 349-362 ◽  
Author(s):  
John L Vogel
Keyword(s):  
Real Time ◽  
The United States ◽  
Monitoring Systems ◽  
Climatic Region ◽  
Convective Storm ◽  
Rainfall Prediction ◽  
Water Quality Regulations ◽  
General Data ◽  
Real Time Information ◽  
Time Information

Continued growth of urban regions and more stringent water quality regulations have resulted in an increased need for more real-time information about past, present, and future patterns and intensities of precipitation. Detailed, real-time information about precipitation can be obtained using radar and raingages for monitoring and prediction of precipitation amounts. The philosophy and the requirements for the development of real-time radar prediction-monitoring systems are described for climatic region similar to the Midwest of the united States. General data analysis and interpretation techniques associated with rainfall from convective storm systems are presented.

scholarly journals Machine Learning Solutions for Bridge Scour Forecast Based on Monitoring Data

2021 ◽  
pp. 036119812110126
Author(s):  
Negin Yousefpour ◽  
Steve Downie ◽  
Steve Walker ◽  
Nathan Perkins ◽  
Hristo Dikanski
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring Data ◽  
Sensor Data ◽  
Monitoring Systems ◽  
Bridge Scour ◽  
Scour Monitoring ◽  
Novel Approach ◽  
Bayesian Inference Method ◽  
Water Level Variations

Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.

scholarly journals Groundwater Monitoring Systems to Understand Sea Water Intrusion Dynamics in the Mediterranean: The Neretva Valley and the Southern Venice Coastal Aquifers Case Studies

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 561
Author(s):  
Ivan Lovrinović ◽  
Alessandro Bergamasco ◽  
Veljko Srzić ◽  
Chiara Cavallina ◽  
Danko Holjević ◽  
...  
Keyword(s):  
Real Time ◽  
Coastal Aquifers ◽  
Sea Water ◽  
Quantitative Information ◽  
Water Dynamics ◽  
Monitoring Systems ◽  
Driving Mechanism ◽  
Sea Water Intrusion ◽  
Water Intrusion ◽  
Aquifer Systems

Sea water intrusion (SWI) has been widely recognized as a global problem, significantly influencing coastal aquifers, mostly through reduced water quality and agricultural production indicators. In this paper, we present the outcomes of the implementation of two independent real-time monitoring systems, planned and installed to get insights on groundwater dynamics within the adjacent coastal aquifer systems, one located in the Neretva Valley, southeastern Croatia, the other located south of the Venice lagoon, northeastern Italy. Both systems are presented with technical details and the capacity to observe, store, and transmit (Neretva site) observed values in real-time. Analysis of time series reveals the significant influence of the sea level oscillations onto the observed groundwater electrical conductivity (EC) and piezometric head values, while precipitation rate is detected as a driving mechanism for groundwater parameters in shallow geological units. The installed monitoring systems are shown to be of great importance to provide qualitative and quantitative information on the processes influencing groundwater and surface water dynamics within two coastal systems.

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