scholarly journals Linear regression model documentation and updates for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River, Kansas, July 2012 through September 2019

2021 ◽  
Author(s):  
Thomas J. Williams
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Regression Model ◽  
Real Time ◽  
Linear Regression Model ◽  
Quality Data ◽  
Water Quality Data ◽  
Kansas River

scholarly journals THE CONTRIBUTION OF GIS TO DISPLAY AND ANALYZE THE WATER QUALITY DATA COLLECTED BY A WIRELESS SENSOR NETWORK: CASE OF BOUREGREG CATCHMENT, MOROCCO

2017 ◽  
Vol IV-4/W4 ◽  
pp. 331-334
Author(s):  
S. Boubakri ◽  
H. Rhinane
Keyword(s):  
Water Quality ◽  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Geographic Information Systems ◽  
Real Time ◽  
Quality Parameters ◽  
Wireless Sensor ◽  
Quality Data ◽  
Catchment Scale ◽  
Water Quality Data

The monitoring of water quality is, in most cases, managed in the laboratory and not on real time bases. Besides this process being lengthy, it doesn’t provide the required specifications to describe the evolution of the quality parameters that are of interest. This study presents the integration of Geographic Information Systems (GIS) with wireless sensor networks (WSN) aiming to create a system able to detect the parameters like temperature, salinity and conductivity in a Moroccan catchment scale and transmit information to the support station. This Information is displayed and evaluated in a GIS using maps and spatial dashboard to monitor the water quality in real time.

scholarly journals PENGEMBANGAN SISTEM DATABASE ONLINE MONITORING (OnLimo) KUALITAS AIR

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Heru Dwi Wahjono
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Online Monitoring ◽  
Water Source ◽  
Underground Water ◽  
Decision Makers ◽  
Quality Data ◽  
Base System ◽  
Water Quality Data ◽  
Processing Water

Recent water quality decrease has caused difficult in finding clean water source for people and their daily life. Monitoring on water quality had been carried out many times, from up stream to down stream. It’s necessary to do Online Monitoring on ground and underground water quality continuously, so that the effect of water quality decrease could be detected earlier and handle directly. The output of water quality data needs to be processed so that the society and the decision makers could see the information publicly. So, we need a design of structured database of online and real-time water quality data processing. Water quality data management using structured data base system could make water source data retracing easier. Katakunci : database struktur, online monitoring, real time monitoring 

scholarly journals Predicting COD and BOD Parameters of Greywater Using Multivariate Linear Regression

2021 ◽  
Author(s):  
Samir Sadik Shaikh ◽  
Rekha Shahapurkar
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Information Sources ◽  
Multivariate Linear Regression ◽  
Wide Scope ◽  
Quality Outcomes ◽  
Greywater Reuse ◽  
Variable Information

Greywater reuse furthermore, reusing can be an incredible method to get non-consumable water. Since it contains broke down pollutions, greywater can’t be utilized straightforwardly. As an outcome, it is critical to decide the nature of water prior to utilizing it. Body estimations require five days to finish, while COD estimations require only a couple of hours. Not exclusively improve models for evaluating water quality are required; however, a more coordinated methodology is additionally getting more normal. Most of these models require a wide scope of information that isn’t in every case promptly available, making it a costly and tedious activity. Because of different issues in the enlistment with estimation included in water quality boundaries like BOD as well as COD, the principal objective of this investigation is to track down the best multivariate direct relapse models for foreseeing complex water quality outcomes. The code was written in Python for multi-variable information sources, and a Linear Regression Model was created. The projected COD versus estimated COD chart shows that the noticed and expected qualities are practically the same. The R-squared worth was 0.9973. A plot of extended BOD as an element of COD is likewise remembered for the outcome.

scholarly journals Recent Advances in Information and Communications Technology (ICT) and Sensor Technology for Monitoring Water Quality

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 510 ◽  
Author(s):  
Jungsu Park ◽  
Keug Tae Kim ◽  
Woo Hyoung Lee
Keyword(s):  
Water Quality ◽  
Water Resources ◽  
Real Time ◽  
Information And Communications Technology ◽  
Quality Data ◽  
Sensor Technology ◽  
Communications Technology ◽  
Water Quality Data ◽  
Recent Advances ◽  
Sensor Monitoring

Water quality control and management in water resources are important for providing clean and safe water to the public. Due to their large area, collection, analysis, and management of a large amount of water quality data are essential. Water quality data are collected mainly by manual field sampling, and recently real-time sensor monitoring has been increasingly applied for efficient data collection. However, real-time sensor monitoring still relies on only a few parameters, such as water level, velocity, temperature, conductivity, dissolved oxygen (DO), and pH. Although advanced sensing technologies, such as hyperspectral images (HSI), have been used for the areal monitoring of algal bloom, other water quality sensors for organic compounds, phosphorus (P), and nitrogen (N) still need to be further developed and improved for field applications. The utilization of information and communications technology (ICT) with sensor technology shows great potential for the monitoring, transmission, and management of field water-quality data and thus for developing effective water quality management. This paper presents a review of the recent advances in ICT and field applicable sensor technology for monitoring water quality, mainly focusing on water resources, such as rivers and lakes, and discusses the challenges and future directions.

scholarly journals Multi-linear regression model for chlorine consumption by waters

2020 ◽  
Vol 26 (4) ◽  
pp. 200402-0
Author(s):  
Guocheng Zhu ◽  
Shanshan Zhang ◽  
Yongning Bian ◽  
Andrew S Hursthouse
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Drinking Water Treatment ◽  
Model Combination ◽  
Water Matrix ◽  
Matrix Properties ◽  
Multi Linear Regression ◽  
The Relationship

In drinking water treatment, disinfection is a key step to ensure the safety of water quality and people's health but little is known of the relationship between chlorine consumption and water matrix properties from varied sources (BWM). In this study, we measured the fluorescence from fractions of NOM (FFN) for the relevant BWM. This included the evaluation of three components: the chlorine-dependence factor (CDF) (DOC and NH3-N), the BWM (such as NO3<sup>-</sup>, NO2<sup>-</sup> and turbidity), and FFN (I-V fluorescence fractions). Multi-linear regression model was used to fit the data. Results showed that when using the CDF, BWM and FNN, in the prediction of chlorine consumption showed the (R<sup>2</sup>) values were 0.72, 0.71 and 0.41, respectively. While the FNN did not fit the model well it did enhance the model using CDF by 11.26%. The FNN is not effective in enhancement of the BWM response to the model. Combination of the CDF, BWM and FNN or that of the CDF and BWM were both effective in prediction of chlorine consumption.

Analysis of Water Quality of Lake Hachiroko in Japan Using a Fuzzy Multiple Regression Model with ALOS AVNIR-2 Data

2016 ◽  
Vol 20 (6) ◽  
pp. 992-1003 ◽  
Author(s):  
Dejian Wang ◽  
◽  
Yoichi Kageyama ◽  
Makoto Nishida ◽  
Hikaru Shirai ◽  
...  
Keyword(s):  
Water Quality ◽  
Regression Model ◽  
Multiple Regression ◽  
Near Infrared ◽  
Multiple Regression Model ◽  
Fuzzy Regression ◽  
Quality Data ◽  
Water Quality Data ◽  
Using Data

The distribution of water pollution is often assessed by remote sensing. In this study, we develop a fuzzy multiple regression model and analyze water quality using data collected by the Advanced Visible and Near Infrared Radiometer type-2 (AVNIR-2) of the Advanced Land Observing Satellite at different time points. We conduct a fuzzy multiple regression analysis of the AVNIR-2 data and direct measurements of the local water quality of Lake Hachiroko in Japan. The relationship between the AVNIR-2 and water quality data are analyzed by solving both min and max problems. We compare the estimated water quality maps with the actual distributions in the study area, and determine that the proposed method enables us to derive water quality conditions effectively from the AVNIR-2 data. Furthermore, by comparing maps created using AVNIR-2 data collected at different times, we obtain results revealing temporal changes in water quality. In addition, we compare maps created using the fuzzy multiple regression and fuzzy regression models. We demonstrate that the former offers a greater number of solutions and provides more details about water quality.

scholarly journals Real-Time Water Quality Monitoring System- Analysis Of Pashan Lake, Maharashtra, India

2019 ◽  
Vol 8 (6) ◽  
pp. 2331-2335 ◽  
Keyword(s):  
Water Quality ◽  
Real Time ◽  
System Analysis ◽  
Water Quality Monitoring ◽  
Remote Access ◽  
Quality Monitoring ◽  
Quality Data ◽  
Water Quality Data ◽  
Notification System ◽  
Quality Status

Pashan Lake in Pune, Maharashtra, India is one of the ancient man-made lakes constructed during British era majorly as a source of water supply for the neighboring colony. Over a while, the lake has switnessed severe degradation of water quality owing to heavy deforestation on neighboring hills, hyacinth formation, industrial effluents, and various anthropogenics activities. A consistent rise in pollution is reported, making the lake water non-potable. Recently, the monitoring and analysis of the lake's water quality status are under consideration to check the suitability of water for drinking. Further, this can aid in planning suitable measures to reduce pollution levels. To address such need of real-time water quality data aforementioned, this paper proposes an online portable water quality monitoring and notification system. An Internet of things(IoT) based platform has been developed with the ability to sense, record, process and wirelessly transmit water quality data. Such platforms enable remote access to data about quality status of any water resource. Further, the developed system has been deployed in Pashan Lake and the results so obtained have been discussed.

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