Weighted Least Square Fitting Based Abnormal Aquaculture Water Quality Perception Data Elimination

2012 ◽  
Vol 10 (1) ◽  
pp. 529-534 ◽  
Author(s):  
Zhenbo Li ◽  
Jun Yue ◽  
Liang Hu ◽  
Daoliang Li ◽  
Zetian Fu
Keyword(s):  
Water Quality ◽  
Least Square ◽  
Quality Perception ◽  
Least Square Fitting ◽  
Weighted Least Square ◽  
Aquaculture Water ◽  
Water Quality Perception

scholarly journals Managing Water Scarcity Futures: Identifying Factors Influencing Water Quality, Risk Perception and Daily Practices in Urban Environments after the Introduction of Desalination

Water ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 2738
Author(s):  
Rubén Alejandro Villar-Navascués ◽  
Maria Christina Fragkou
Keyword(s):  
Water Quality ◽  
Risk Perception ◽  
Water Supply ◽  
Global Scale ◽  
Water Supply Systems ◽  
Urban Water ◽  
Urban Water Supply ◽  
Quality Perception ◽  
Desalinated Water ◽  
Water Quality Perception

During the last two decades on a global scale, there has been a significant development of desalination as a strategy to ensure the urban water supply in arid and semi-arid areas. Beyond issues related to the higher economic costs, one of the main barriers that may limit this water source’s development is its supposed negative water quality perception. This research aims to understand better which factors are behind water quality perception in Antofagasta (Chile), where desalinated water was introduced in 2003. Since then, this urban water supply system has increasingly incorporated desalination, creating three parallel areas according to the water sources used in each of them (desalinated water, freshwater and a mix of both). To do so, more than 800 questionnaires to test water quality perception and water consumption habits were conducted in households. Up to six logistic regression models have been implemented to identify which variables better explain water quality satisfaction, risk perception and daily water practices considering the water supply area. It is worth noting that most of this type of research has been carried out in study cases with homogeneous urban water supply systems with conventional water resources. Results indicate that, among other factors, organoleptic water characteristics, such as taste, and socioeconomic status are some of the main factors that explain the perception of water quality and daily practices. In addition, a lower water quality perception and greater risk perception have been identified where desalinated water has been introduced, which makes some households develop averting behaviors to improve water quality, such as boil water.

scholarly journals Unpulsed Optical Emission from the Crab Pulsar

2000 ◽  
Vol 177 ◽  
pp. 297-298
Author(s):  
Aaron Golden ◽  
Andy Shearer ◽  
Gregory Beskin
Keyword(s):  
Light Curve ◽  
Power Law ◽  
High Speed ◽  
Optical Emission ◽  
Least Square ◽  
Crab Pulsar ◽  
Least Square Fitting ◽  
Weighted Least Square ◽  
Power Law Exponent

AbstractUsing the high speed 2-d TRIFFID photometer, we have obtained phase resolved photometry of the Crab pulsar inUBVthat allows us to flux the unpulsed light curve component. Following de-extinction, weighted least-square fitting indicates a power-law exponent ofα= −0.62 ± 0.49. This is steeper than that reported for the peak components and its origin remains unclear with respect to contemporary magnetospheric theory.

B-Spline Surface Reconstruction from Cloudy Data Using Weighted Least Square Fitting

2015 ◽  
Vol 740 ◽  
pp. 629-632
Author(s):  
Zi Zhi Lin ◽  
Si Hui Shu
Keyword(s):  
Approximation Error ◽  
Least Square ◽  
Base Surface ◽  
Positive Weight ◽  
Least Square Fitting ◽  
Weighted Least Square ◽  
Spline Surface ◽  
The Times ◽  
Key Techniques ◽  
Surface Construction

In this paper, we present an algorithm for surface approximation to the measured cloudy data with four boundaries. Several key techniques about this algorithm are also described, such as base surface construction, projection and weighted least square approximation. We use weighted least square to reduce the times of iterations, because the iteration is a very expensive and error prone process. We add a positive weight to each point, and the weight-adding algorithm is introduced as well. Increasing the weight onto this point will decrease the approximation error of the point. Finally, some examples of this algorithm demonstrate its effectiveness and validity.

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