Journal articles: 'Data processing Water'

1

Kim, Young-jun. "Long Offset Seismic Data Processing for Deep Water Hydrocarbon Survey of Ulleung Basin, East Sea, Korea." Journal of the Korean Society of Mineral and Energy Resources Engineers 50, no. 1 (2013): 1. http://dx.doi.org/10.12972/ksmer.2013.50.1.001.

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Sonnenberg, H., M. Rustler, M. Riechel, N. Caradot, P. Rouault, and A. Matzinger. "Best data handling practices in water-related research." Water Practice and Technology 8, no. 3-4 (September 1, 2013): 390–98. http://dx.doi.org/10.2166/wpt.2013.039.

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Data play an important role in water-related research. Based on experiences in data collection and data processing in water-related research this paper proposes – both from a computer scientist's and an environmental engineer's point of view – a set of rules for data handling: Rule 1: Protect raw data; Rule 2: Save metadata; Rule 3: Use databases; Rule 4: Separate data from processing; Rule 5: Use programming; Rule 6: Avoid redundancy; Rule 7: Be transparent; Rule 8: Use standards and naming conventions. Applying these rules (i) increases the quality of data and results, (ii) allows to prepare data for long-term usage and make data accessible to different people, (iii) makes data processing transparent and results reproducible, and (iv) saves – at least in the long run – time and effort. With this contribution the authors would like to start a discussion about best data handling practices and present a first checklist of data handling and data processing for practitioners and researchers working in the water sector.

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Zhaochunlei. "Research on big data processing of water conservancy automation." IOP Conference Series: Earth and Environmental Science 768, no. 1 (May 1, 2021): 012114. http://dx.doi.org/10.1088/1755-1315/768/1/012114.

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4

Matsumoto, Yukio. "Processing, storage and use of water quality monitoring data." Japan journal of water pollution research 10, no. 5 (1987): 282–86. http://dx.doi.org/10.2965/jswe1978.10.282.

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Machell, J., S. R. Mounce, B. Farley, and J. B. Boxall. "Online data processing for proactive water distribution network operation." Drinking Water Engineering and Science Discussions 6, no. 2 (August 28, 2013): 261–90. http://dx.doi.org/10.5194/dwesd-6-261-2013.

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Abstract. Operational benefits and efficiencies generated using prevalent water industry methods and techniques are becoming more difficult to achieve; as demonstrated by English and Welsh water companies' static position with regards the economic level of leakage. Water companies are often unaware of network incidents such as burst pipes or low pressure events until they are reported by customers; and therefore use reactive strategies to manage the effects of these events. It is apparent that new approaches need to be identified and applied to promote proactive network management if potential operational productivity and standards of service improvements are to be realised. This paper describes how measured flow and pressure data from instrumentation deployed in a water distribution network was automatically gathered, checked, analysed and presented using recently developed techniques to generate apposite information about network performance. The work demonstrated that these technologies can provide early warning, and hence additional time to that previously available, thereby creating opportunity to proactively manage a network; for example to minimise the negative impact on standards of customer service caused by unplanned events such as burst pipes. Each method, applied individually, demonstrated improvement on current industry processes. Combined application resulted in further improvements; including quicker and more localised burst main location. Future possibilities are explored, from which a vision of seamless integration between such technologies emerges to enable proactive management of distribution network events.

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6

Ai, Ping, and Zhao Xin Yue. "A Framework for Processing Water Resources Big Data and Application." Applied Mechanics and Materials 519-520 (February 2014): 3–8. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.3.

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The development of information technology expands the spatial and temporal scale and types of elements of the water resources information, making the water resources data show the characteristics of multi-source, heterogeneous, massive, and the traditional data processing method is difficult for fine processing and dynamic analysis. Combined with the "4v" characteristics of big data, we put forward a framework for processing water resources big data, to process and analyze modern water resources data for real-time and rapid, and discuss the related application. Based on the features of modern water resources data, this paper discusses the characteristics and application technology of big data, and briefly describes a framework for processing water resources big data and application.

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Zhou, Zong Guo, Yin Xia Lou, and Jian Chu. "The Error Analysis and Data Processing of Leaf Water Potential." Advanced Materials Research 219-220 (March 2011): 1440–44. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1440.

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For the implementation of precision irrigation (PI), it is most important to measure precisely plant water potential. The traditional measuring instruments still can not meet the need for continuous automatic detection of plant water potential, and have difficulty detecting living plant water potential. Plant water potential soft- sensing is one of the ways worth exploring. The first thing we should think about is the accuracy of the data. Filter the information of plant water potential acquired by the detecting system and further analyze this information to discover the change rules on the plant water potential.

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Slijkerman, W. F. J., W. J. Looyestijn, P. Hofstra, and J. P. Hofman. "Processing of Multi-Acquisition NMR Data." SPE Reservoir Evaluation & Engineering 3, no. 06 (December 1, 2000): 492–97. http://dx.doi.org/10.2118/68408-pa.

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Summary Crucial issues in formation evaluation are the determination of porosity, permeability, hydrocarbon volumes, and net-to-gross ratio. Nuclear magnetic resonance (NMR) logging provides measurements that are directly related to these parameters. The NMR response of fluids contained in pores is governed by their T2- and T1-relaxation times, diffusion coefficient, and whether or not they wet the rock. In the case where fluids possess a sufficiently large contrast in these properties and NMR data have been acquired with suitably chosen acquisition parameters (i.e., wait times and/or inter-echo times) a separation of water, oil, and gas NMR responses can be made. From these separate NMR responses the hydrocarbon volumes, porosity, and permeability estimates are subsequently calculated. Key in these applications is the ability to include all the acquired log NMR data into the processing towards the desired end result. Methods exist to derive hydrocarbon volumes from T2 distributions or from echo decay data. However, these are all methods in which the difference between just two acquisitions that only differ in either wait time or inter-echo time are considered. Over the past years we have developed, tested, and employed an alternative processing technique named multi-acquisition NMR (MacNMR). MacNMR takes any number of log acquisitions (wait time and/or inter-echo time variations) and simultaneously inverts them using a rigorous forward model to derive the desired water and hydrocarbon T2 distributions. In this paper, we discuss the concepts of MacNMR and demonstrate its versatility in NMR log processing. An example will illustrate its benefits. Introduction This paper discusses the method used by Shell to process multi-acquisition nuclear magnetic resonance (NMR) data. The objective of the processing is to extract fluid volumes and properties from multi-acquisition NMR data. The potential of multi-acquisition NMR logging for water, oil, and gas discrimination and volume quantification was recognized already in 1993. At that time no commercial processing of such data was available. It was decided to develop an in-house multi-acquisition processing capability. From 1993 to 1996 the development effort was focused on the evaluation of potential processing concepts and the development of the necessary mathematical algorithms. In 1996 the actual software implementation was developed, and in October 1996 first results were available and published internally. In March 1997 a company-wide beta test of the software was organized. In August 1997 the software was released company wide and has been in use since then. Multi-Acquisition Data Processing Methods As an introduction, we briefly review methods for quantitative processing of multi-acquisition NMR data that are described in the open literature. We make the distinction between methods that operate in the relaxation time domain vs. methods that operate in the acquisition time domain. Analysis in the Relaxation Time (or T2) Domain. Here, methods are discussed that operate in the T2 domain. Differential Spectrum Method. The differential spectrum method, first published by Akkurt and Vinegar1 works on dual-wait-time data. The concept is to independently T2 invert the long- and short-wait-time echo-decay vectors into a T2 spectrum. The two resulting T2 spectra are subtracted and, provided the wait times have been selected suitably,2 the difference between the two T2 spectra only arises from fluids with long T1 components (usually hydrocarbons). Volumes are quantified by integrating the difference T2 spectrum and correcting for the polarization difference between long and short wait time. Enhanced Diffusion Method. The enhanced diffusion method, recently published by Akkurt et al., 3 exploits the diffusion contrast between the diffusive brine and the less diffusive (medium-to-heavy) oil (i.e., water diffusion is faster than oil diffusion). The idea is that the inter-echo time is chosen sufficiently long such that the water and oil signals are fully separated in the T2 domain (i.e., water is at lower T2 than oil). Determining oil volumes is then just a matter of integrating over the appropriate T2 range in the T2 spectrum. Analysis in the Acquisition Time Domain. Here, methods are discussed that operate in the acquisition time domain. Time-Domain Analysis. The time-domain analysis method (TDA) operates on dual-wait-time data. This method was first published by Prammer et al.4 The concept is to subtract the measured long- and short-wait-time decay vectors into an echo difference. In case the wait times have been chosen suitably2 the difference of the two decay vectors should be arising from a long T1 component (usually a hydrocarbon). This difference echo vector is subsequently T2 inverted (using "matched filters," which basically means that a uni- or bi-exponential is fitted to the data). In that way, only the T2 component arising from the hydrocarbon is found. The hydrocarbon volume is deduced by correcting the resulting signal strength from the difference in polarization between long and short wait time. Echo Ratio Method. This method, published by Flaum et al.,5 works on dual-inter-echo-time data. The long- and short-inter-echo-time echo decays are divided and an apparent diffusion coefficient is calculated. The apparent diffusion coefficient can be used as a qualitative indicator for the presence of gas. MacNMR Method MacNMR uses a method that is radically different from the other processing schemes and is a comprehensive implementation of earlier concepts.1,6 MacNMR employs a forward model to model the measured echo-decay vectors. The starting points in the forward model are the T2 spectra for each of the fluids present (water, oil, and/or gas) that would be measured at infinite wait time and zero gradient. From these T2 spectra, echo-decay vectors are constructed by accounting for the effects of hydrogen index, polarization, and diffusion. The best-fit T2 spectra are found by inverting the forward model to the measured echo-decay vectors. All measured echo-decay vectors included in the inversion are treated on an equal statistical footing. They are weighted with their respective rms-noise values. Hence, decays with the lowest noise contribute most. In principle, any number of echo-decay vectors can be included in the inversion. The current software implementation of MacNMR accepts up to a maximum of six echo-decay vectors, totaling a maximum of 7,000 echoes. The inversion typically takes less than 1 second per depth increment. In a sense, MacNMR employs a very classical concept in that it defines unknown variables (T2 spectra for the fluids present) that are determined from the available data (i.e., all the acquired decay vectors) by error minimization. Between the unknown variables and the data is a forward model. The forward model contains the effects of inter-echo-time variation and wait-time variation. Analysis in the Relaxation Time (or T2) Domain. Here, methods are discussed that operate in the T2 domain. Differential Spectrum Method. The differential spectrum method, first published by Akkurt and Vinegar1 works on dual-wait-time data. The concept is to independently T2 invert the long- and short-wait-time echo-decay vectors into a T2 spectrum. The two resulting T2 spectra are subtracted and, provided the wait times have been selected suitably,2 the difference between the two T2 spectra only arises from fluids with long T1 components (usually hydrocarbons). Volumes are quantified by integrating the difference T2 spectrum and correcting for the polarization difference between long and short wait time. Enhanced Diffusion Method. The enhanced diffusion method, recently published by Akkurt et al.,3 exploits the diffusion contrast between the diffusive brine and the less diffusive (medium-to-heavy) oil (i.e., water diffusion is faster than oil diffusion). The idea is that the inter-echo time is chosen sufficiently long such that the water and oil signals are fully separated in the T2 domain (i.e., water is at lower T2 than oil). Determining oil volumes is then just a matter of integrating over the appropriate T2 range in the T2 spectrum. Analysis in the Acquisition Time Domain. Here, methods are discussed that operate in the acquisition time domain. Time-Domain Analysis. The time-domain analysis method (TDA) operates on dual-wait-time data. This method was first published by Prammer et al.4 The concept is to subtract the measured long- and short-wait-time decay vectors into an echo difference. In case the wait times have been chosen suitably2 the difference of the two decay vectors should be arising from a long T1 component (usually a hydrocarbon). This difference echo vector is subsequently T2 inverted (using "matched filters," which basically means that a uni- or bi-exponential is fitted to the data). In that way, only the T2 component arising from the hydrocarbon is found. The hydrocarbon volume is deduced by correcting the resulting signal strength from the difference in polarization between long and short wait time. Echo Ratio Method. This method, published by Flaum et al.,5 works on dual-inter-echo-time data. The long- and short-inter-echo-time echo decays are divided and an apparent diffusion coefficient is calculated. The apparent diffusion coefficient can be used as a qualitative indicator for the presence of gas.

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9

TAKIGUCHI, Hideki, Hiroshi TAKAMATSU, Shunsuke UCHIDA, Kenkichi ISHIGURE, Motonori NAKAGAMI, and Makoto MATSUI. "Water Chemistry Data Acquisition, Processing, Evaluation and Diagnostic Systems in Light Water Reactors." Journal of Nuclear Science and Technology 41, no. 2 (February 2004): 214–25. http://dx.doi.org/10.1080/18811248.2004.9715478.

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10

Mali, Sheetal. "Tipping Bucket Rain Gauge Data Processing System: A Review." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 102–6. http://dx.doi.org/10.22214/ijraset.2021.37914.

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Abstract: The tipping bucket system consists of funnel which collects the water of the rain in a container which is like a seesaw type module which tips side by side and collects the water. When the level of the water decreases below a preset level, the lever changes its side, causing the collected water to dump in a vessel and electrical signal is sent. By this system the high, medium or heavy rainfall character can be obtained. The rainfall character is calculated by the rainfall in 1 hour and corresponding number of pulses clicking in a period of 10 minutes. Various types of tipping bucket systems are reviewed by using rainfall and snow precipitation, using internet enabling, using rain drop imaging and artificial intelligence and also using wireless sensor network and GSM data transmission. Tipping Bucket is the most useful parameter for measuring the rainfall. In this way the rainfall is measured using the Tipping Bucket Rain Gauge System.

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11

Machell, J., S. R. Mounce, B. Farley, and J. B. Boxall. "Online data processing for proactive UK water distribution network operation." Drinking Water Engineering and Science 7, no. 1 (April 2, 2014): 23–33. http://dx.doi.org/10.5194/dwes-7-23-2014.

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Abstract. Operational benefits and efficiencies generated using prevalent water industry methods and techniques are becoming more difficult to achieve; as demonstrated by English and Welsh water companies' static position with regards the economic level of leakage. Water companies are often unaware of network incidents such as burst pipes or low pressure events until they are reported by customers; and therefore use reactive strategies to manage the effects of these events. It is apparent that new approaches need to be identified and applied to promote proactive network management if potential operational productivity and standards of service improvements are to be realised. This paper describes how measured flow and pressure data from instrumentation deployed in a UK water distribution network was automatically gathered, checked, analysed and presented using recently developed techniques to generate apposite information about network performance. The work demonstrated that these technologies can provide early warning, and hence additional time to that previously available, thereby creating opportunity to proactively manage a network; for example to minimise the negative impact on standards of customer service caused by unplanned events such as burst pipes. Each method, applied individually, demonstrated improvement on current industry processes. Combined application resulted in further improvements; including quicker and more localised burst main location. Future possibilities are explored, from which a vision of seamless integration between such technologies emerges to enable proactive management of distribution network events.

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12

Zhao, Meng-hua, and Xiao-peng Chen. "A Combined Data Processing Method on Water Impact Force Measurement." Journal of Hydrodynamics 24, no. 5 (October 2012): 692–701. http://dx.doi.org/10.1016/s1001-6058(11)60293-x.

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Liu, Huaishan, Yi Hu, Yanxin Yin, Linfei Wang, Siyou Tong, and Hai Ma. "Shallow water body data processing based on the seismic oceanography." Journal of Ocean University of China 12, no. 3 (July 25, 2013): 319–26. http://dx.doi.org/10.1007/s11802-013-2100-5.

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YADAV, J. K. S., R. K. GIRI, and L. R. MEENA. "Error handling in GPS data processing." MAUSAM 62, no. 1 (December 14, 2021): 97–102. http://dx.doi.org/10.54302/mausam.v62i1.212.

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We are aware that the processing of GPS data through GAMIT processing software is not free from errors. Some of them are generated due to different modules involved in processing. The data quality depends so many factors, like quality of met-instrument, which supplies the meteorological data, algorithm of processing which based on the network homogeneity or heterogeneity and location of the site, whether it is free from multi-path etc. The root mean square errors for New Delhi, Mumbai, Kolkata, Guwahati and Chennai GPS stations are spatially correlated and observations are weighted according to the satellite elevation angle. Diurnal variability of Integrated Precipitable Water Vapour (IPWV) has been shown its range from 45 mm to 65 mm for New Delhi during the monsoon season, 2008.

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Liu, Ming Tang, Li Bin Fu, Yan Hui Xin, and Li Li. "Application of Data Fusion Based on Least Squares in Sediment Concentration Data Processing." Advanced Materials Research 181-182 (January 2011): 1064–68. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.1064.

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In the process of measuring the sediment concentration in flow-water, the temperature in the water will greatly influence the output of the capacitive differential pressure sensor. This paper uses least squares method to fuse the results of experiment to eliminate influence of the temperature and depth． The system uses value of the capacitive differential pressure sensor, the temperature sensor and the depth sensor as input . This paper particularly introduces principle of the least squares method, PLC hardware design and multi-channels data fusion technology. The results indicate that data fusion method based least squares can obtain accurate and steady outputs.

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Holinde, L., T. H. Badewien, J. A. Freund, E. V. Stanev, and O. Zielinski. "Processing of water level derived from water pressure data at the Time Series Station Spiekeroog." Earth System Science Data Discussions 8, no. 1 (April 13, 2015): 345–64. http://dx.doi.org/10.5194/essdd-8-345-2015.

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Abstract. The quality of water level time series data strongly varies with periods of high and low quality sensor data. In this paper we are presenting the processing steps which were used to generate high quality water level data from water pressure measured at the Time Series Station (TSS) Spiekeroog. The TSS is positioned in a tidal inlet between the islands of Spiekeroog and Langeoog in the East Frisian Wadden Sea (southern North Sea). The processing steps will cover sensor drift, outlier identification, interpolation of data gaps and quality control. A central step is the removal of outliers. For this process an absolute threshold of 0.25 m/10 min was selected which still keeps the water level increase and decrease during extreme events as shown during the quality control process. A second important feature of data processing is the interpolation of gappy data which is accomplished with a high certainty of generating trustworthy data. Applying these methods a 10 years dataset of water level information at the TSS was processed and the results were submitted to WDC MARE data base system PANGAEA (

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Holinde, L., T. H. Badewien, J. A. Freund, E. V. Stanev, and O. Zielinski. "Processing of water level derived from water pressure data at the Time Series Station Spiekeroog." Earth System Science Data 7, no. 2 (October 27, 2015): 289–97. http://dx.doi.org/10.5194/essd-7-289-2015.

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Abstract. The quality of water level time series data strongly varies with periods of high- and low-quality sensor data. In this paper we are presenting the processing steps which were used to generate high-quality water level data from water pressure measured at the Time Series Station (TSS) Spiekeroog. The TSS is positioned in a tidal inlet between the islands of Spiekeroog and Langeoog in the East Frisian Wadden Sea (southern North Sea). The processing steps will cover sensor drift, outlier identification, interpolation of data gaps and quality control. A central step is the removal of outliers. For this process an absolute threshold of 0.25 m 10 min−1 was selected which still keeps the water level increase and decrease during extreme events as shown during the quality control process. A second important feature of data processing is the interpolation of gappy data which is accomplished with a high certainty of generating trustworthy data. Applying these methods a 10-year data set (December 2002–December 2012) of water level information at the TSS was processed resulting in a 7-year time series (2005–2011). Supplementary data are available at doi:10.1594/PANGAEA.843740.

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Gong, Yi Cheng, Yong Xiang Zhang, Fei Ding, Wei Chun Gao, and Yi Fan Wang. "Water Environment Monitoring System Based on Data Extraction - Transmission and Processing." Advanced Materials Research 779-780 (September 2013): 1592–95. http://dx.doi.org/10.4028/www.scientific.net/amr.779-780.1592.

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The current paper describes a water environment monitoring system, which is focused on the online monitoring techniques of groundwater detection, data process and transmission, a local experiment equipment is also developed. Besides, an integrated study, in terms of host computer of the groundwater monitoring system, programmable logic controller (PLC) control system and sensor technology is carried out. Moreover, the secondary data process of automatic detection instrumentation and display technology is integrated, as well as the visualized data platform is primarily established. The water environment monitoring system improves the standard of monitoring indicators from the surface water environment to groundwater environment. The detection interval of the system is less than 3min, and the pollution forecast scope surpasses 16 km2. A high accuracy of the forecasting and risk analysis can be obtained.

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Kim, Hyeon-gyu, Minjun Kim, Rongtao Gao, and Myong-ho Park. "Processing and interpretation of shallow-water seismic data for CO2 injection." ASEG Extended Abstracts 2015, no. 1 (December 2015): 1–4. http://dx.doi.org/10.1071/aseg2015ab258.

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Selitrenikov, Alexey V., Yuri E. Zevatskii, and Denis V. Samoylov. "METHOD OF DATA PROCESSING OF CONDUCTOMETRIC TITRATION IN WATER-ORGANIC MEDIA." Bulletin of the Saint Petersburg State Institute of Technology (Technical University) 54(80) (2020): 3–14. http://dx.doi.org/10.36807/1998-9849-2020-54-80-3-14.

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LeBlanc, L. R., and P. P. J. Beaujean. "Spatio-temporal processing of coherent acoustic communication data in shallow water." IEEE Journal of Oceanic Engineering 25, no. 1 (January 2000): 40–51. http://dx.doi.org/10.1109/48.820735.

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Jonna, S., K. V. S. Badarinath, and J. Saibaba. "Digital image processing of Remote Sensing data for water quality studies." Journal of the Indian Society of Remote Sensing 17, no. 2 (June 1989): 59–64. http://dx.doi.org/10.1007/bf02991911.

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23

Byrne, Shannon, Brian Clifford, Walter Simmons, Jan Depner, Barbara Reed, Jenny Moestikiwati, and Gail Smith. "Processing Data for Seafloor Mapping: Integration and Metrics." Marine Technology Society Journal 35, no. 4 (December 1, 2001): 20–32. http://dx.doi.org/10.4031/002533201788058017.

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Today's state-of-the-art hydrographic survey instrumentation produces higher resolution and more densely sampled measurements than were available in the past. This supports improvements in the definition of seafloor features and characteristics, however, it also places more stringent requirements on the systems used to process seafloor survey data. In shallow water environments bathymetric sampling rates can exceed 4000 soundings per second and data from Digital Side-Scan Sonar Systems can exceed 1 Gb/hr. In support of the Second International Conference on High-Resolution Surveys in Shallow Water, and working in cooperation with Reson Inc., Goleta, Ca., and the University of New Hampshire's Center for Coastal and Ocean Mapping (CCOM), Science Applications International Corporation (SAIC), Newport, RI conducted a survey of the conference common data set test area in Portsmouth Harbor using a Reson 8125 dual-head sonar system. The acquired data were made available as part of the conference common dataset. An area-based approach to data cleaning, including the use of an automated filter for spike detection, is presented. Resource and effort metrics associated with the processing of samples from the common data set are provided. This includes corrector application, data cleaning, validation, and quality control. Results from the area-based approach are compared with results from a traditional line-oriented approach. Three bathymetric datasets from Portsmouth Harbor are compared and the results reported.

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Martí, Aniol, Jordi Portell, David Amblas, Ferran de Cabrera, Marc Vilà, Jaume Riba, and Garrett Mitchell. "Compression of Multibeam Echosounders Bathymetry and Water Column Data." Remote Sensing 14, no. 9 (April 25, 2022): 2063. http://dx.doi.org/10.3390/rs14092063.

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Over the past decade, Multibeam Echosounders (MBES) have become one of the most used techniques in sea exploration. Modern MBES are capable of acquiring both bathymetric information on the seafloor and the reflectivity of the seafloor and water column. Water column imaging MBES surveys acquire significant amounts of data with rates that can exceed several GB/h depending on the ping rate. These large file sizes obtained from recording the full water column backscatter make remote transmission difficult if not prohibitive with current technology and bandwidth limitations. In this paper, we propose an algorithm to decorrelate water column and bathymetry data, focusing on the KMALL format released by Kongsberg Maritime in 2019. The pre-processing stage is integrated into FAPEC, a data compressor originally designed for space missions. Here, we test the algorithm with three different datasets: two of them provided by Kongsberg Maritime and one dataset from the Gulf of Mexico provided by Fugro USA Marine. We show that FAPEC achieves good compression ratios at high speeds using the pre-processing stage proposed in this paper. We also show the advantages of FAPEC over other lossless compressors as well as the quality of the reconstructed water column image after lossy compression at different levels. Lastly, we test the performance of the pre-processing stage, without the constraint of an entropy encoder, by means of the histograms of the original samples and the prediction errors.

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Martí, Aniol, Jordi Portell, David Amblas, Ferran de Cabrera, Marc Vilà, Jaume Riba, and Garrett Mitchell. "Compression of Multibeam Echosounders Bathymetry and Water Column Data." Remote Sensing 14, no. 9 (April 25, 2022): 2063. http://dx.doi.org/10.3390/rs14092063.

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Over the past decade, Multibeam Echosounders (MBES) have become one of the most used techniques in sea exploration. Modern MBES are capable of acquiring both bathymetric information on the seafloor and the reflectivity of the seafloor and water column. Water column imaging MBES surveys acquire significant amounts of data with rates that can exceed several GB/h depending on the ping rate. These large file sizes obtained from recording the full water column backscatter make remote transmission difficult if not prohibitive with current technology and bandwidth limitations. In this paper, we propose an algorithm to decorrelate water column and bathymetry data, focusing on the KMALL format released by Kongsberg Maritime in 2019. The pre-processing stage is integrated into FAPEC, a data compressor originally designed for space missions. Here, we test the algorithm with three different datasets: two of them provided by Kongsberg Maritime and one dataset from the Gulf of Mexico provided by Fugro USA Marine. We show that FAPEC achieves good compression ratios at high speeds using the pre-processing stage proposed in this paper. We also show the advantages of FAPEC over other lossless compressors as well as the quality of the reconstructed water column image after lossy compression at different levels. Lastly, we test the performance of the pre-processing stage, without the constraint of an entropy encoder, by means of the histograms of the original samples and the prediction errors.

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Song, Hongwei, Ming Li, Chaoquan Wu, Qingchuan Wang, Shunke Wei, Mingxing Wang, and Wenhui Ma. "Data-Driven Methodology for the Prediction of Fluid Flow in Ultrasonic Production Logging Data Processing." Geofluids 2022 (March 15, 2022): 1–15. http://dx.doi.org/10.1155/2022/5637971.

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A new method for the determination of oil and water flow rates in vertical upward oil-water two-phase pipe flows has been proposed. This method consists of an application of machine learning techniques on the probability density function (PDF) and the power spectral density (PSD) of the power spectrum output of an ultrasonic Doppler sensor in the pipe. The power spectrum characteristic parameters of the two-phase flow are first determined by the probability density function (PDF) method. Then, the transducer signal is preprocessed by distance correlation analysis (DCA), and independent features are extracted by principal component analysis (PCA). The extracted features are used as input to a least-squares fit, which gave the oil flow rates as output. In the same way, the transducer signal is also preprocessed by partial correlation analysis (PCA), and independent features were extracted using independent component analysis (ICA). The extracted features were used as inputs to multilayer back-propagation neural networks, which water cuts as output. The present method was used to calibrate an ultrasonic Doppler sensor to estimate the flow rates of both phases in oil–water flow in a vertical pipe of diameter 159 mm. Predictions of the present method were in good agreement with direct flow rate measurements. Compared to previously used methods of feature extraction from the ultrasonic Doppler power spectrum signals, the present method provides a theoretical basis for the interpretation of ultrasonic multiphase flow logging data. Ultrasonic multiphase flow logging has potential application value in the production profile logging and interpretation evaluation of production wells with low fluid production and high water cut.

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Hicham, Jamil, Jamal Elhassan, El Mansouri Bouabid, Moumen Aniss, and Chao Jamal. "Processing and decisions relating to water resources data: The case of Morocco." SHS Web of Conferences 119 (2021): 03007. http://dx.doi.org/10.1051/shsconf/202111903007.

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The national water strategy has been an essential vector of government strategy for a long time. The management of water resources is an integral part of the economic development of Morocco. Nevertheless, the definition of the strategic axes of this component and the adequate decision-making depends directly on the collection and use of all the data relating to water resources. If big data technologies present a suitable solution to ensure optimal and rapid use of its data, the success of functional and technical designs can only be provided after total control of the processing and decision-making processes relating to the water domain. In this paper, we will try to identify the aspects relating to the processes of data collection, processing, consolidation, and decision-making through the use of the results of field surveys and interviews with business managers.

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Zhao, Liangbin, Guoyou Shi, and Jiaxuan Yang. "Ship Trajectories Pre-processing Based on AIS Data." Journal of Navigation 71, no. 5 (April 22, 2018): 1210–30. http://dx.doi.org/10.1017/s0373463318000188.

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Data derived from the Automatic Identification System (AIS) plays a key role in water traffic data mining. However, there are various errors regarding time and space. To improve availability, AIS data quality dimensions are presented for detecting errors of AIS tracks including physical integrity, spatial logical integrity and time accuracy. After systematic summary and analysis, algorithms for error pre-processing are proposed. Track comparison maps and traffic density maps for different types of ships are derived to verify applicability based on the AIS data from the Chinese Zhoushan Islands from January to February 2015. The results indicate that the algorithms can effectively improve the quality of AIS trajectories.

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S, Kanaga Suba Raja, Kumar R S, Balaji V, and Usha Kiruthika S. "Satellite Image Processing for Water Detection." ECS Transactions 107, no. 1 (April 24, 2022): 16183–90. http://dx.doi.org/10.1149/10701.16183ecst.

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Water resources have a major impact in different day to day activities. Whether it is consuming water or for commercial purposes, gallons of water have been used all over the world. In order to use the resource to the fullest, it should be planned properly and should have effective water management techniques. Satellite image processing is one of the most effective ways of detecting water on the earth’s surface. By receiving the images from the satellite, we will be able to easily detect water. But, due to minor effects, we may face difficulties in differentiating the characteristics of water. For example, when there is a shadow of tall buildings on the water surface, it will be difficult to read the image of the water body as the water surface creates a mirror reflection on it. Hence, it is important that we differentiate between water bodies and shadows. Over the years, different researchers conducted experiments to extract data on high resolution satellite images of the water bodies. The main objective of the paper is to look at the various approaches to extract information from different satellite images using satellite image processing.

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Olasz, A., D. Kristóf, M. Belényesi, K. Bakos, Z. Kovács, B. Balázs, and Sz Szabó. "IQPC 2015 TRACK: WATER DETECTION AND CLASSIFICATION ON MULTISOURCE REMOTE SENSING AND TERRAIN DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (August 20, 2015): 583–88. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-583-2015.

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Since 2013, the EU FP7 research project “IQmulus” encourages the participation of the whole scientific community as well as specific user groups in the IQmulus Processing Contest (IQPC). This year, IQPC 2015 consists of three processing tasks (tracks), from which “Water detection and classification on multi-source remote sensing and terrain data” is introduced in the present paper. This processing track addresses a particular problem in the field of big data processing and management with the objective of simulating a realistic remote sensing application scenario. The main focus is on the detection of water surfaces (natural waters, flood, inland excess water, other water-affected categories) using remotely sensed data. Multiple independent data sources are available and different tools could be used for data processing and evaluation. The main challenge is to identify the right combination of data and methods to solve the problem in the most efficient way. Although the first deadline for submitting track solutions has passed and the track has been successfully concluded, the track organizers decided to keep the possibility of result submission open to enable collecting a variety of approaches and solutions for this interesting problem.

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Hsu, Po Hsien, Lie Chung Shen, Ching Liang Tseng, Jaw Fang Lee, and Ding Yu Liu. "Using MATLAB for Processing RGPS Data to Determine the Altitude of Water Surface." Applied Mechanics and Materials 311 (February 2013): 67–72. http://dx.doi.org/10.4028/www.scientific.net/amm.311.67.

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The purpose of this study is to use the software of MATLAB for processing the direct and reflected signals of GPS to monitor altitude of water surface in the water flume, as well as to establish a practicable technique of measuring sea water level. It is because before determining water wave pattern, the feasibility and accuracy of reflected GPS method must be proved. Therefore, there was a field test in the Mid-size wave flume of the Tainan Hydraulics Laboratory, National Cheng Kung University. After improving RGPS positioning procedure, the tranquil water level and the steadily descending of water level observation for one hour were performed. In this research, an integrated GPS receiver that employed direct and reflected GPS signals for the measurement was introduced. Both RHCP and LHCP antennas were employed to simultaneously receive the L1 and L2 carrier phase of direct and reflected signals. After the data analysis of the GPS observation, the position of signal reflection and the water level of the wave were solved. The results of the RGPS, wave gauge, and staff meter were coincided within 0.5cm ~ 1.0 cm. The differences between the reflection heights of this GPS system and the record of wave gauge were almost identical within 90%. It is proved the reflected GPS technique is possible to monitor water surface altitude and determine water wave patterns.

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David Suits, L., TC Sheahan, JW Scholte, JQ Shang, and RK Rowe. "Improved Complex Permittivity Measurement and Data Processing Technique for Soil-Water Systems." Geotechnical Testing Journal 25, no. 2 (2002): 187. http://dx.doi.org/10.1520/gtj11362j.

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Church, Ian. "Multibeam sonar water column data processing tools to support coastal ecosystem science." Journal of the Acoustical Society of America 141, no. 5 (May 2017): 3949. http://dx.doi.org/10.1121/1.4988966.

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Aguilar Muñoz, Viviana, and Denilson Ribeiro Viana. "Application experiment for calculating the water surplus by weather data computer processing." Modelling in Science Education and Learning 8, no. 1 (January 13, 2015): 93. http://dx.doi.org/10.4995/msel.2015.3049.

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Komarov, S. A., V. L. Mironov, A. N. Romanov, and A. V. Yevtyushkin. "REMOTE SENSING OF THE WATER TABLE: MEASUREMENT AND A DATA PROCESSING ALGORITHM." Mapping Sciences and Remote Sensing 36, no. 1 (January 1999): 1–10. http://dx.doi.org/10.1080/07493878.1999.10642103.

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Singh, S., D. Dutta, U. Singh, J. R. Sharma, and V. K. Dadhwal. "Hydat-A Hyperspectral Data Processing Tool for Field Spectroradiometer Data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 481–84. http://dx.doi.org/10.5194/isprsarchives-xl-8-481-2014.

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A hyperspectral data processing tool "HyDAT" has been developed in MATLAB environment for processing of Field Spectroradiometer data for vegetation studies. Several basic functions e.g. data visualization, pre-processing, noise removal and data transformation and features like automatic absorption feature recovery and their characterization have been introduced. A new concept of spectral geometry has been included as a separate module which is conceptualized as triangle formed over spectral space joining the vertices of green reflectance peak, red well and inflection point and is extremely useful for vegetation health analysis. A large variety of spectral indices both static and dynamic, have been introduced which is useful for remote estimation of foliar biochemicals. Keeping in view the computational requirement, MATLAB was used in the programming environment. It has various in-built functions for statistical and mathematical analysis, signal processing functions like FFT (Fast Fourier Transform), CWT (Continuous Wavelet Transform), direct smoothing function for moving average, Savitzky-Golay smoothing technique, etc. which can be used with ease for the signal processing and field data analysis. FSF (Field Spectroscopy Facility) Post processing Toolbox can also be freely downloaded and can be used for the direct importing and pre-processing of Spectroradiometer data for detector overlap correction, erroneous water band removal and smoothing. The complete package of the software has been bundled for standalone application of shared libraries with additional files for end users. The software is powered by creation of spectral library and customized report generation. An online help menu guides the user for performing different functions. The tool is capable of reducing the time required for processing field based hyperspectral data significantly and eliminate the need for different software to process the raw data and spectral features extraction.

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Irnaka, Theodosius Marwan, Wahyudi Wahyudi, Eddy Hartantyo, Adien Akhmad Mufaqih, Ade Anggraini, and Wiwit Suryanto. "SEISGAMA: A Free C# Based Seismic Data Processing Software Platform." International Journal of Geophysics 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/2913591.

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Seismic reflection is one of the most popular methods in geophysical prospecting. Nevertheless, obtaining high resolution and accurate results requires a sophisticated processing stage. There are many open-source seismic reflection data processing software programs available; however, they often use a high-level programming language that decreases its overall performance, lacks intuitive user-interfaces, and is limited to a small set of tasks. These shortcomings reveal the need to develop new software using a programming language that is natively supported by Windows® operating systems, which uses a relatively medium-level programming language (such as C#) and can be enhanced by an intuitive user interface. SEISGAMA was designed to address this need and employs a modular concept, where each processing group is combined into one module to ensure continuous and easy development and documentation. SEISGAMA can perform basic seismic reflection processes. This ability is very useful, especially for educational purposes or during a quality control process (in the acquisition stage). Those processes can be easily carried out by users via specific menus on SEISGAMA’s main user interface. SEISGAMA has been tested, and its results have been verified using available theoretical frameworks and by comparison to similar commercial software.

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Jothiprakash, V., and Alka S. Kote. "Improving the performance of data-driven techniques through data pre-processing for modelling daily reservoir inflow." Hydrological Sciences Journal 56, no. 1 (February 14, 2011): 168–86. http://dx.doi.org/10.1080/02626667.2010.546358.

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Wimmer, Michael H., Markus Hollaus, Günter Blöschl, Andreas Buttinger-Kreuzhuber, Jürgen Komma, Jürgen Waser, and Norbert Pfeifer. "Processing of nationwide topographic data for ensuring consistent river network representation." Journal of Hydrology X 13 (December 2021): 100106. http://dx.doi.org/10.1016/j.hydroa.2021.100106.

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Heister, Anton, and Rolf Scheiber. "Coherent large beamwidth processing of radio-echo sounding data." Cryosphere 12, no. 9 (September 19, 2018): 2969–79. http://dx.doi.org/10.5194/tc-12-2969-2018.

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Abstract. Coherent processing of radio-echo sounding data of polar ice sheets is known to provide an indication of bedrock properties and detection of internal layers. We investigate the benefits of coherent processing of a large azimuth beamwidth to retrieve and characterize the orientation and angular backscattering properties of the surface and subsurface features. MCRDS data acquired over two distinct test areas in Greenland are used to demonstrate the specular backscattering properties of the ice surface and of the internal layers, as well as the much wider angular response of the bedrock. The coupling of internal layers' orientation with the bed topography is shown to increase with depth. Spectral filtering can be used to increase the SNR of the internal layers and mitigate the surface multiple. The variance of the bed backscattering can be used to characterize the bed return specularity. The use of the SAR-focused RES data ensures the correct azimuth positioning of the internal layers for the subsequent slope estimation.

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Filipponi, Federico. "River Color Monitoring Using Optical Satellite Data." Proceedings 2, no. 10 (June 13, 2018): 569. http://dx.doi.org/10.3390/iecg_2018-05336.

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Knowledge of inland water quality and riverine inputs to oceans is fundamental for water management, environmental monitoring, and the definition of policies and planning strategies related to the sustainable use of rivers. While European Union directives aim at the conservation of inland water resources, the ground operational monitoring network is often inadequate. River monitoring using Remote Sensing may complement in-situ measurements, supplying continuous, spatially explicit representation of parameters related to water quality and solid transport, even if the high-frequency dynamics of water parameters may not be caught due to limited satellite revisit time. Sentinel-2 and Landsat-8 satellites, equipped with MSI and OLI optical sensors whose spectral bands perform a more accurate atmospheric correction, allow for the development of methodologies for monitoring river color from space, thanks to high spatial resolution and short revisit times. This study presents a processing chain, developed to monitor water constituents in rivers using high-resolution satellite images. Multi-temporal analysis of chlorophyll-a (Chl-a) and total suspended matter (TSM) bio-geophysical variables was performed for the case study of the Po River (Italy) for the year 2017. Quantitative estimations of water constituents were retrieved from Sentinel-2 optical multispectral satellite data using the C2RCC algorithm, and the main outcomes are discussed. The developed processing chain can be used to create operational services for river monitoring, and represent a major improvement in the identification of spatio-temporal dynamics (like solid transport) in riverine systems.

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Zhong, Yuanchang, Liang Zhang, Shaojing Xing, Fachuan Li, and Beili Wan. "The Big Data Processing Algorithm for Water Environment Monitoring of the Three Gorges Reservoir Area." Abstract and Applied Analysis 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/698632.

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Owing to the increase and the complexity of data caused by the uncertain environment, the water environment monitoring system in Three Gorges Reservoir Area faces much pressure in data handling. In order to identify the water quality quickly and effectively, this paper presents a new big data processing algorithm for water quality analysis. The algorithm has adopted a fast fuzzy C-means clustering algorithm to analyze water environment monitoring data. The fast clustering algorithm is based on fuzzy C-means clustering algorithm and hard C-means clustering algorithm. And the result of hard clustering is utilized to guide the initial value of fuzzy clustering. The new clustering algorithm can speed up the rate of convergence. With the analysis of fast clustering, we can identify the quality of water samples. Both the theoretical and simulated results show that the algorithm can quickly and efficiently analyze the water quality in the Three Gorges Reservoir Area, which significantly improves the efficiency of big data processing. What is more, our proposed processing algorithm provides a reliable scientific basis for water pollution control in the Three Gorges Reservoir Area.

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Hayman, Matthew, Willem Marais, Robert Stillwell, and Scott Spuler. "Poisson Total Variation Denoising for Micropulse Water Vapor DIAL." EPJ Web of Conferences 237 (2020): 06012. http://dx.doi.org/10.1051/epjconf/202023706012.

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We have adapted the Poisson Total Variation lidar signal processing technique for Micro-Pulse DIAL water vapor estimates. This processing technique ingests data at 10 second-37.5 meter resolution where it adaptively adjusts the retrieval resolution based on signal-to-noise and performs range deconvolution. The result is high resolution data in regions with ample signal, preservation of sharp discontinuities where they exist, and a data product that better represents of the acquired photon counting data than the standard processing technique.

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Kanaya, T., K. Hirabayashi, I. Fujita, and K. Tsumura. "Detection of unusual data in online monitoring of wastewater processing." Water Science and Technology 33, no. 1 (January 1, 1996): 71–79. http://dx.doi.org/10.2166/wst.1996.0007.

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A basic of process control is to understand process conditions with measuring instruments and to operate processes so as to realize target conditions. If input measured values were inaccurate, output of manipulated variables would become improper and, as a result, it would be difficult to bring the process to the desired condition. In the wastewater treatment process, thanks to the latest progress in sensor technology, numerous automatic measuring instruments have been introduced. However, because of adverse environmental conditions peculiar to the wastewater treatment process such as slime-contaminated sensing elements, long-term continuous measurement is rather difficult. We believe such disadvantages in the measurement are making automatic control of the process very difficult to achieve. Under such circumstances, we have developed a detection system for unusual data which automatically checks six items of deviation from upper and lower limit values, rate of change (too much or too little), collating data from similar measuring instruments, etc. based on the measuring data of the last 30 days. With this system, validity of the accumulated data is being checked using measuring data. Accordingly, it enables us to deal with characteristics of measuring instruments, situations of wastewater treatment plants, seasonal changes, etc. automatically. In this report, automatic methods to establish judgement criteria, structure of this detection system and logic of detection of unusual data are introduced. Furthermore, test results with the data collected from actual wastewater treatment plants are covered.

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Aronoff, Stan, and Hugh Parliament. "From data to information ‐ image processing for decision making." Geocarto International 2, no. 3 (September 1987): 25–30. http://dx.doi.org/10.1080/10106048709354104.

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Wen, Ya Yuan, Wen Ming Huang, Jie Wu, Yue Chen, and Ji Qing Song. "Water Consumption Analysis System Based on Data Mining." Applied Mechanics and Materials 241-244 (December 2012): 1093–97. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1093.

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As knowledge discovery in databases, data mining means a process of extraction potentially useful information from data in databases, which can be applied to information management, query processing, decision making, process control etc. Those are urgently needed to improve efficient management in water supply industry, since water has been recognized by governments worldwide as a scarce resource. In response to such demand, this paper proposes a software application, which designed to accessing to database, operating the data mining, and output the results and charts. We analyzed the different prediction models and designed the water consumption system, which has two functions of analysis of possible correlations between the water consumption and nature of the industry and prediction on future water consumption. As the system built, the paper provides samples and produces the results and analysis.

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Horscroft, T. R., and J. E. Bain. "Validation of seismic data processing and interpretation with integration of gravity and magnetic data." Geological Society, London, Special Publications 99, no. 1 (1996): 5–9. http://dx.doi.org/10.1144/gsl.sp.1996.099.01.02.

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Periola, Ayodele, Akintunde Alonge, and Kingsley Ogudo. "Space Habitat Data Centers—For Future Computing." Symmetry 12, no. 9 (September 10, 2020): 1487. http://dx.doi.org/10.3390/sym12091487.

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Data from sensor-bearing satellites requires processing aboard terrestrial data centres that use water for cooling at the expense of high data-transfer latency. The reliance of terrestrial data centres on water increases their water footprint and limits the availability of water for other applications. Therefore, data centres with low data-transfer latency and reduced reliance on Earth’s water resources are required. This paper proposes space habitat data centres (SHDCs) with low latency data transfer and that use asteroid water to address these challenges. The paper investigates the feasibility of accessing asteroid water and the reduction in computing platform access latency. Results show that the mean asteroid water access period is 319.39 days. The use of SHDCs instead of non-space computing platforms reduces access latency and increases accessible computing resources by 11.9–33.6% and 46.7–77% on average, respectively.

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Anantha Padmanabha, E., P. Shashivardhan Reddy, B. Narender, S. Muralikrishnan, and V. K. Dadhwal. "Photogrammetric processing of hexagon stereo data for change detection studies." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-8 (November 27, 2014): 151–57. http://dx.doi.org/10.5194/isprsannals-ii-8-151-2014.

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Hexagon satellite data acquired as a part of USA Corona program has been declassified and is accessible to general public. This image data was acquired in high resolution much before the launch of civilian satellites. However the non availability of interior and exterior orientation parameters is the main bottle neck in photogrammetric processing of this data. In the present study, an attempt was made to orient and adjust Hexagon stereo pair through Rigorous Sensor Model (RSM) and Rational Function Models (RFM). The study area is part of Western Ghats in India. For rigorous sensor modelling an arbitrary camera file is generated based on the information available in the literature and few assumptions. A terrain dependent RFM was generated for the stereo data using Cartosat-1 reference data. The model accuracy achieved for both RSM and RFM was better than one pixel. DEM and orthoimage were generated with a spacing of 50 m and Ground Sampling Distance (GSD) of 6 m to carry out the change detection with a special emphasis on water bodies with reference to recent Cartosat-1 data. About 72 new water bodies covering an area of 2300 hectares (23 sq. km) were identified in Cartosat-1 orthoimage that were not present in Hexagon data. The image data from various Corona programs like Hexagon provide a rich source of information for temporal studies. However photogrammetric processing of the data is a bit tedious due to lack of information about internal sensor geometry.

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Wang Bin, 王斌, and 杨慧中 Yang Huizhong. "A Processing Method for Spectral Data of Online Total Phosphorus Detection in Water." Laser & Optoelectronics Progress 52, no. 4 (2015): 043002. http://dx.doi.org/10.3788/lop52.043002.

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Journal articles on the topic 'Data processing Water'

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