scholarly journals Flowmeter data validation and reconstruction methodology to provide the annual efficiency of a water transport network: the ATLL case study in Catalonia

2013 ◽  
Vol 6 (1) ◽  
pp. 79-95 ◽  
Author(s):  
J. Quevedo ◽  
J. Pascual ◽  
V. Puig ◽  
J. Saludes ◽  
R. Sarrate ◽  
...  
Keyword(s):  
Water Transport ◽  
Transport Network ◽  
Information Storage ◽  
Data Validation ◽  
Network Efficiency ◽  
Data Set ◽  
Flow Measurements ◽  
Automatic Data ◽  
Daily Data ◽  
Hydraulic Network

<p><strong>Abstract.</strong> The object of this paper is to provide a flowmeter data validation/reconstruction methodology that determines the annual economic and hydraulic efficiency of a water transport network. In this paper, the case of Aigües Ter Llobregat (ATLL) company, that is in charge of managing the 80% of the overall water transport network in Catalonia (Spain), will be used for illustrating purposes. The economic/hydraulic network efficiency is based on the daily data set collected by the company using about 200 flowmeters of the network. The data collected using these sensors are used by the remote control and information storage systems and they are stored in a relational database. All the information provided by ATLL is analyzed to detect inconsistent data using an automatic data validation method deployed in parallel with the evaluation of the network efficiency. As a result of the validation process, corrections of flow measurements and of the volume of billed water are introduced. The results of the ATLL water transport network obtained during year 2010 will be used to illustrate the approach proposed in this paper.</p>

Flowmeter data validation and reconstruction methodology to provide the annual efficiency of a water transport network: the ATLL case study in Catalonia

2013 ◽  
Vol 14 (2) ◽  
pp. 337-346
Author(s):  
J. Quevedo ◽  
J. Pascual ◽  
V. Puig ◽  
J. Saludes ◽  
R. Sarrate ◽  
...  
Keyword(s):  
Water Transport ◽  
Transport Network ◽  
Information Storage ◽  
Water Volume ◽  
Data Validation ◽  
Network Efficiency ◽  
Data Set ◽  
Flow Measurements ◽  
Automatic Data ◽  
Daily Data

The object of this paper is to provide a flowmeter data validation/reconstruction methodology that determines the annual economic efficiency of a water transport network. In this paper, the case of Aigües Ter Llobregat (ATLL) company, which manages 80% of the overall water transport network in Catalonia (Spain), will be used for illustrating purposes. Economic network efficiency is based on daily data set collected by the company using about 200 flowmeters of the network. Data collected using these sensors are used by remote control and information storage systems and they are stored in a relational database. All information provided by ATLL is analysed to detect inconsistent data using an automatic data validation method deployed in parallel with the network efficiency evaluation. As a result of the validation process, corrections of flow measurements and of billed water volume are introduced. Results from ATLL water transport network corresponding to year 2010 will be used to illustrate the approach proposed in this paper.

scholarly journals Big Data for Health Care Analytics using Extreme Machine Learning Based on Map Reduce

2020 ◽  
Vol 9 (3) ◽  
pp. 2758-2762
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Storage ◽  
Clinical Data ◽  
Disease Risk ◽  
Learning Algorithm ◽  
Information Storage ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Data Set

A large volume of datasets is available in various fields that are stored to be somewhere which is called big data. Big Data healthcare has clinical data set of every patient records in huge amount and they are maintained by Electronic Health Records (EHR). More than 80 % of clinical data is the unstructured format and reposit in hundreds of forms. The challenges and demand for data storage, analysis is to handling large datasets in terms of efficiency and scalability. Hadoop Map reduces framework uses big data to store and operate any kinds of data speedily. It is not solely meant for storage system however conjointly a platform for information storage moreover as processing. It is scalable and fault-tolerant to the systems. Also, the prediction of the data sets is handled by machine learning algorithm. This work focuses on the Extreme Machine Learning algorithm (ELM) that can utilize the optimized way of finding a solution to find disease risk prediction by combining ELM with Cuckoo Search optimization-based Support Vector Machine (CS-SVM). The proposed work also considers the scalability and accuracy of big data models, thus the proposed algorithm greatly achieves the computing work and got good results in performance of both veracity and efficiency.

Hydrological functioning of irrigated maize crops in southwest France using Eddy Covariance measurements and a land surface model

2021 ◽  
Author(s):  
Oluwakemi Dare-Idowu ◽  
Lionel Jarlan ◽  
Aurore Brut ◽  
Valerie Le-Dantec ◽  
Vincent Rivalland ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Land Surface ◽  
Sap Flow ◽  
Heat Fluxes ◽  
Added Value ◽  
Surface Model ◽  
Data Set ◽  
Flow Measurements ◽  
Ground Heat Flux

&lt;p&gt;This study aims to analyze the main components of the energy and hydric budgets of irrigated maize in southwestern France. To this objective, the ISBA-A-gs (&lt;span&gt;Interactions between Soil, Biosphere, and Atmosphere) &lt;/span&gt;is run over six maize growing seasons. As a preliminary step, the ability of the ISBA-A-gs model to predict the different terms of the energy and water budgets is assessed thanks to a large database of &lt;em&gt;in situ&lt;/em&gt; measurements by comparing the single budget version of the model with the new Multiple Energy Balance version solving an energy budget separately for the soil and the vegetation. The &lt;em&gt;in situ&lt;/em&gt; data set acquired at the Lamasquere site (43.48&lt;sup&gt;o&lt;/sup&gt; N, 1.249&lt;sup&gt;o&lt;/sup&gt; E) includes half-hourly measurements of sensible (H) and latent heat fluxes (LE) estimated by an Eddy Covariance system. Measurements also include net radiation (Rn), ground heat flux (G), plant transpiration with sap flow sensors, meteorological variables, and 15-days measurements of vegetation characteristics. The seasonal dynamics of the turbulent fluxes were properly reproduced by both configurations of the model with an R&amp;#178; ranging from 0.66 to 0.89, and a root mean square error lower than 48 W m&lt;sup&gt;-2&lt;/sup&gt;. Statistical metrics showed that H was better predicted by MEB with R&amp;#178; of 0.80 in comparison to ISBA-Ags (0.73). However, the difference between the RMSE of ISBA-Ags and MEB during the well-developed stage of the plants for both H and LE does not exceed 8 W m&lt;sup&gt;-2&lt;/sup&gt;. This implies that MEB only has a significant added value over ISBA-Ags when the soil and the canopy are not fully coupled, and over a heterogeneous field. Furthermore, this study made a comparison between the sap flow measurements and the transpiration simulated by ISBA-A-gs and MEB. A good dynamics was reproduced by ISBA-A-gs and MEB, although, MEB (R&amp;#178;= 0.91) provided a slightly more realistic estimation of the vegetation transpiration. Consequently, this study investigated the dynamics of the water budget during the growing maize seasons. Results indicated that drainage is almost null on the site, while the observed values of cumulative evapotranspiration that was higher than the water inputs are related to a shallow ground table that provides supplement water to the crop. This work provides insight into the modeling of water and energy exchanges over maize crops and opens perspectives for better water management of the crop in the future.&lt;/p&gt;

Estimating kinetic energy for therapy sessions

2018 ◽  
pp. 44
Author(s):  
Alejandro Romero ◽  
Raymundo Cornejo ◽  
Fernando Martínez ◽  
Claudia Barraza ◽  
Vania Álvarez
Keyword(s):  
Kinetic Energy ◽  
Human Body ◽  
Input Device ◽  
Global Evaluation ◽  
Motion Sensing ◽  
Data Set ◽  
Daily Observation ◽  
Preliminary Results ◽  
Daily Data ◽  
Therapy Sessions

Therapists usually base the progress of the patients on a daily observation with a global evaluation every six months. This scenario could create a lack of exact and measurable daily data. Measurable data could be very helpful for monitoring patient’s progress and take important decisions regarding the therapy activities. A way to calculate the movement of a human body is through its kinetic energy. The kinetic energy measures the amount of energy spent in a therapy session or activity. The present study calculates the kinetic energy spent by children during movement based learning therapy through a data set captured with a motion sensing input device. Our preliminary results indicate an important impact on kinetic energy during this type of therapy

Do Global Financial, Oil and Gold Volatility Shocks Affect the GCC Stock Markets?

2019 ◽  
Vol 5 (2) ◽  
pp. 157-175 ◽  
Author(s):  
Abdullah Alqahtani
Keyword(s):  
Saudi Arabia ◽  
Stock Market ◽  
Stock Markets ◽  
Structural Var ◽  
Causality Test ◽  
Data Set ◽  
Financial Volatility ◽  
Daily Data ◽  
Positive Shock ◽  
The Impact

This study employed the non-structural VAR econometrics approach to examine the impact of Global Oil (OVX), Financial (VIX), and Gold (GVZ) volatility indices on GCC stock markets using a daily data set spanning from January 5, 2009 to August 16, 2018. From the VAR result obtained, disequilibrium in the global financial volatility (VIX) was able to significantly transmit negative shock to Bahrain and Kuwait stock markets and positive shock on GVZ. While the global Gold volatility was capable of transmitting fairly positive shock to the UAE and VIX market. The OLS also revealed more to the result obtained from VAR as it shows that OVX and VIX can have impact on the GCC stock markets. The causality test revealed that there is a unidirectional causality running from Qatar and UAE to OVX; none of the variables was able to granger cause VIX, while unidirectional causality exist from VIX and UAE to GVZ and VIX and Qatar to Bahrain. VIX and Qatar can granger cause Kuwait stock market, and only Saudi Arabia and Oman have bidirectional causality. Unidirectional causality exists from Saudi Arabia to Qatar, and Qatar is the only stock market capable of causing UAE unidirectionally. Hence, the study concludes that VIX and GVZ are capable of transmitting shocks to three of the six GCC stock markets—(Bahrain, Kuwait and The UAE) negatively (Bahrain and Kuwait) and positively (The UAE). And on this note, the study recommends that appropriate financial and gold transaction policies should be institutionalized so as to mitigate the transmission of shocks into the markets. Also, financial and gold experts who regulate the stock and gold markets especially in Bahrain and Kuwait should watch for any abnormality changes in the volatility movement of the financial and gold markets.

scholarly journals Empirical Model for Estimating Global Solar Radiation on Horizontal Surfaces for Selected Cities in the Six Geopolitical Zones in Nigeria

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
M. S. Okundamiya ◽  
A. N. Nzeako
Keyword(s):  
Solar Radiation ◽  
Global Solar Radiation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Data Set ◽  
Ambient Temperatures ◽  
Performance Accuracy ◽  
Daily Data ◽  
Proposed Model ◽  
Regression Theory

This study proposes a temperature-based model of monthly mean daily global solar radiation on horizontal surfaces for selected cities, representing the six geopolitical zones in Nigeria. The modelling was based on linear regression theory and was computed using monthly mean daily data set for minimum and maximum ambient temperatures. The results of three statistical indicators: Mean Bias Error (MBE), Root Mean Square Error (RMSE), andt-statistic (TS), performed on the model along with practical comparison of the estimated and observed data, validate the excellent performance accuracy of the proposed model.

scholarly journals THE REVIEW OF THE MONOGRAPH “TRANSPORT: TECHNOLOGIES, ECONOMY, ENVIRONMENT, HEALTH”

Transport ◽  
2003 ◽  
Vol 18 (6) ◽  
pp. la-ld
Author(s):  
Habil Algirdas Jurkauskas
Keyword(s):  
Road Network ◽  
Transport Network ◽  
Information Storage ◽  
Small Scale ◽  
Long Distance ◽  
The Road ◽  
Long Distance Transport ◽  
Monitoring And Forecasting ◽  
Passenger Flows ◽  
Distance Transport

From the first sight the improvement of transport technologies seems to be simple replacement of old transport means/vehicles by new ones. However, even if the most advanced transport means are purchased, they may be used absolutely inefficiently. Therefore, besides transport means it is necessary to improve the entire technological process of transportation. For this purpose many kinds of research have to be carried out, such as: the identification of freight and passenger flows, their constant monitoring and forecasting, the optimisation of the road network and transport flows in it, the optimisation of freight origin and distribution places, etc. Abroad there are many publications concerning the optimisation of long distance transport network and transportation of goods weighing 20–40 tons. However, actually there is no research of such cases when the weight of freight is 50–1500 kg and the transport network is in a small scale region, let us say in a town, under the conditions of congestion of the road network and ecological restrictions. It was attempted to propose certain solutions in this field: 1) the methodology of statistical analysis of freight flows and forecasting was proposed; 2) the problem of information storage is usually encountered in using statistical information on freight flows, for this purpose relevant algorithms for the solution of this problem are proposed.

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