scholarly journals Groundwater nitrate monitoring network optimization with missing data

2004 ◽  
Vol 40 (2) ◽  
Author(s):  
L. M. Nunes ◽  
E. Paralta ◽  
M. C. Cunha ◽  
L. Ribeiro
Keyword(s):  
Missing Data ◽  
Network Optimization ◽  
Monitoring Network

scholarly journals Water quality assessment based on multivariate statistics and water quality index of a strategic river in the Brazilian Atlantic Forest

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
David de Andrade Costa ◽  
José Paulo Soares de Azevedo ◽  
Marco Aurélio dos Santos ◽  
Rafaela dos Santos Facchetti Vinhaes Assump
Keyword(s):  
Water Quality ◽  
Water Resources ◽  
Network Optimization ◽  
Water Quality Index ◽  
Quality Index ◽  
Monitoring Network ◽  
Water Quality Assessment ◽  
Pollution Source ◽  
Components Analysis ◽  
Government Data

AbstractFifty-four water samples were collected between July and December 2019 at nine monitoring stations and fifteen parameters were analysed to provide an updated diagnosis of the Piabanha River water quality. Further, forty years of monitoring were analysed, including government data and previous research projects. A georeferenced database was also built containing water management data. The Water Quality Index from the National Sanitation Foundation (WQINSF) was calculated using two datasets and showed an improvement in overall water quality, despite still presenting systematic violations to Brazilian standards. Principal components analysis (PCA) showed the most contributing parameters to water quality and enabled its association with the main pollution sources identified in the geodatabase. PCA showed that sewage discharge is still the main pollution source. The cluster analysis (CA) made possible to recommend the monitoring network optimization, thereby enabling the expansion of the monitoring to other rivers. Finally, the diagnosis provided by this research establishes the first step towards the Framing of water resources according to their intended uses, as established by the Brazilian National Water Resources Policy.

Safety Monitoring and Evaluation of Construction Projects Based on Multi-sensor Fusion

2020 ◽  
Vol 19 (6) ◽  
pp. 431-441
Author(s):  
Na Ni
Keyword(s):  
Missing Data ◽  
Monitoring System ◽  
Sensor Fusion ◽  
Construction Projects ◽  
Monitoring Network ◽  
Safety Monitoring ◽  
Monitoring And Evaluation ◽  
Construction Safety ◽  
Monitoring Data ◽  
Safety Monitoring System

For most construction projects, the complex engineering environment, the backward data collection technology, and the unreasonable monitoring network have resulted in many problems in monitoring data such as lots of noise and missing data items, therefore, it is of great significance to study the safety monitoring system of construction projects based on wireless sensor network (WSN). For this reason, this paper proposed a construction safety monitoring and evaluation (CSME) model based on multi-sensor fusion. First, the system structure and data flow model of the construction safety monitoring system were constructed; then, combining with a multi-sensor deep fusion system which was built on physical and information systems, this paper designed a spectrum sensing algorithm for sensor signals within the construction area. After that, tempo-spatial correlation analysis was conducted on the monitoring data, and a multi-sensor monitoring network joint sparse (MSMN-JS) model was constructed, which realized reconstruction of missing data items. At last, this paper used experimental results to prove the application value of the algorithm model to the safety monitoring and evaluation of construction projects.

Active Learning for Monitoring Network Optimization

2012 ◽  
pp. 285-318 ◽  
Author(s):  
Devis Tuia ◽  
Alexei Pozdnoukhov ◽  
Loris Foresti ◽  
Mikhail Kanevski
Keyword(s):  
Active Learning ◽  
Network Optimization ◽  
Monitoring Network

scholarly journals GNSS Monitoring Network Optimization Case Study: Opak Fault Deformation, Yogyakarta

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Nurrohmat Widjajanti ◽  
Sherly Shinta Emalia ◽  
Parseno Parseno
Keyword(s):  
High Precision ◽  
Network Optimization ◽  
Fault Location ◽  
Monitoring Network ◽  
Parameter Method ◽  
Station Coordinates ◽  
Cofactor Matrix ◽  
Least Squares Adjustment ◽  
Gnss Network ◽  
Monitoring Stations

Opak fault is a fault located in Opak River area, Bantul. The existence of the fault is one of the biggest causes of earthquake in Yogyakarta in 2006. The seismic potential caused by the active fault requires continuous geodynamic monitoring. The GNSS network (TGD, SGY, and OPK) have been developed since 2013 consists of 17 stations and in 2016 there was an additional number of four monitoring stations. Several high-precision monitoring stations distributed at the fault location are needed to monitor the fault movement. Optimal observation network is one of the factors to obtain high precision station coordinates. The GNSS network optimization has been carried out in the previous research partially on each network; namely the segment of TGD, SGY, and OPK. Therefore, this research conducts a thoroughly optimization for 17 monitoring stations either use old or new stations to obtain an optimal network based on the criteria of accuracy and reliability.The network is designed widely from simple to complex combination and to combination between network segments. The computation uses least squares adjustment with parameter method. The value of the cofactor matrix parameter of the adjustment is applied to analyze the network based on the function of the accuracy criteria, namely A-Optimality, D-Optimality, E-Optimality, S-Optimality, and I-Optimality. Meanwhile, the value of the residual cofactor matrix is used for network configuration analysis based on the reliability objective function, namely the individual redundancy, external and internal reliabilities criteria. The result showed that the design of TGD, SGY and OPK network segments are optimized based on the criteria of accuracy and reliability if they use a network design with a complex baseline. The criteria for accuracy and reliability in the design with a combination of segments such as TGD and SGY, TGD and OPK, as well as TGD, SGY, and OPK are not much different from the optimization results performed by each segment. Therefore, if the measurements are carried out with a limited receiver, it is better to use each of segment designs.

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