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3 edition of Multiple regression modeling approach for regional water quality management found in the catalog.

Multiple regression modeling approach for regional water quality management

Environmental Research Laboratory (Athens, Ga.)

Multiple regression modeling approach for regional water quality management

by Environmental Research Laboratory (Athens, Ga.)

  • 7 Want to read
  • 35 Currently reading

Published by Environmental Protection Agency, Office of Research and Development, Environmental Research Laboratory, for sale by the National Technical Information Service in Athens, Ga, Springfield, Va .
Written in English

    Subjects:
  • Regression analysis -- Mathematical models,
  • Water quality management -- Mathematical models,
  • Regional planning

  • Edition Notes

    Statementby David J. Lystrom ... [et al.], Geological Survey, U.S. Department of the Interior
    SeriesInteragency energy-environment research and development program report ; EPA-600/7-78-198, Research reporting series -- EPA-600/7-78-198
    ContributionsLystrom, David J, Geological Survey (U.S.)
    The Physical Object
    Paginationvii, 60 p :
    Number of Pages60
    ID Numbers
    Open LibraryOL14207685M

    Kristan Cockerill, Vincent Tidwell, Lacy Daniel and Amy Sun, Environmental Reviews & Case Studies: Engaging the Public and Decision Makers in Cooperative Modeling for Regional Water Management, Environmental Practice, /S, 12, 4, (), (). A regional regression model was developed to estimate the spatial distribution of ground water recharge in subhumid regions. The regional regression recharge (RRR) model was based on a regression of basin-wide estimates of recharge from surface water drainage basins, precipitation, growing degree days (GDD), and average basin specific yield (SY).File Size: KB.

    strength of the relationship between variables, while regression attempts to describe that relationship between these variables in more detail. B. The linear regression model (LRM) The simple (or bivariate) LRM model is designed to study the relationship between a pair of variables that appear in a data set. The multiple LRM is designed to File Size: KB. Water Quality Modeling in Reservoirs Using Multivariate Linear Regression and Two Neural Network Models Wei-BoChen 1 andWen-ChengLiu 2,3 National Science and Technology Center for Disaster Reduction, New Taipei City, Taiwan Department of Civil and Disaster Prevention Engineering, National United University, Miaoli, TaiwanCited by:

    A convenient way to interpret regression model computed concentrations in the context of water-quality criteria is the probability of exceedance. Probability of exceedance is a single value representing the percent likelihood that a criterion has been exceeded (Eq 7). SUSTAINABLE WATER RESOURCES MANAGEMENT: INSIGHTS FOR WATER QUALITY POLICY IN THE GREAT LAKES REGION", Dissertation, Michigan Technological University, Follow this and additional works at: Part of the Water Resource Management Commons. Masthead Cited by: 5.


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Multiple regression modeling approach for regional water quality management by Environmental Research Laboratory (Athens, Ga.) Download PDF EPUB FB2

The multiple-regression approach provides a means of estimating water- quality characteristics at unsampled stream sites and of estimating the gen- eral effects of natural and cultural aspects of drainage basins on water qual- ity.

Multiple regression modeling approach for regional water quality management Author: David J Lystrom ; Frank A Rinella ; David A Rickert ; Lisa Zimmerman ; Environmental Research Laboratory. This simple Stream Temperature Modeling and Monitoring approach uses thermograph data and geomorphic predictor variables from GIS software and digital elevation models (DEM).

Multiple regression models are used to predict stream temperature metrics throughout a stream network with moderate accuracy (R 2 ~ ). The models can provide basic descriptions of spatial patterns in. Multiple regression models are a type of empirical model that is widely used in hydrology in situations where sufficient historical data are available to develop statistical relationships between the variable of interest and the hydrologic variables.

In this study, two artificial neural network models (i.e., a radial basis function neural network, RBFN, and an adaptive neurofuzzy inference system approach, ANFIS) and a multilinear regression (MLR) model were developed to simulate the DO, TP, Chl a, and SD in the Mingder Reservoir of central Taiwan.

The input variables of the neural network and the MLR models were determined using linear Cited by: A robust modeling approach for regional water management under multiple uncertainties Author links open overlay panel Y.P.

Li a G.H. Huang a 1 S.L. Nie b 2 X. Chen c 3 Show moreCited by: Regionalization of Lumped Water Balance Model Parameters Based on Multiple Regression Article in Journal of Hydrology (s 1–4)– June with Reads How we measure 'reads'.

A regional regression model was developed to map groundwater recharge across the Lower Mekong Basin where agricultural water demand is increasing, especially during the dry season.

As well as all its parameter like pH, Dissolved Oxygen, Chloride, Total Dissolved Solid, Total Alkalinity, Calcium, Magnesium, Total Hardness, Nitrate and Electrical Conductivity should be within acceptable limit.

A novel approach of regression method is adopted to assess quality of Size: KB. The proposed models can be useful for planning land use controls in integrated water quality management program.

As water quality of flowing water is. This paper deals with water quality management using statistical analysis and time-series prediction model. The monthly variation of water quality standards has been used to compare statistical mean, median, mode, standard deviation, kurtosis, skewness, coefficient of variation at Yamuna River.

Model validated using R-squared, root mean square error, mean absolute percentage Cited by: Models, Second Edition Christensen: Linear Models for Multivariate, Time Series, and Spatial Data Christensen: Log-Linear Models and Logistic Regression, Second Edition Creighton: A First Course in Probability Models and Statistical Inference Dean and Voss: Design and Analysis of Experiments du Toit, Steyn, and Stumpf: Graphical Exploratory.

More detail can be found in books solely devoted to water quality modeling (Chapra and Reckhow ; Chapra ; McCutcheon ; Thomann and Mueller ; Orlob ; Schnoor ) as well as the current literature. Model Data. Data availability and accuracy is of concern in the development and use of models for water quality by: 2.

The first step in improving the predictive properties of regression models of water use would be to enhance the quality of the data used in estimating the model parameters. Indeed, one of the advantages to regression approaches is that they may reveal cause.

The main techniques for developing those models of landscape-water quality are statistical regression analysis based on linear models. In this article, Allometric models and the traditional multiple linear regression models for estimating the linkage between landscape metrics and water quality were tested in Sihu Basin, Hubei Province, by: 6.

stepwise regression models for quartile 1, median and quartile 61 Table Total counts of flow related, natural and anthropogenic predictor variables associated with the stepwise multiple regression models for each water qualityFile Size: 2MB. Object-Oriented Modeling Approach to Surface Water Quality Management.

Environmental Modeling and Software 21(5) pp Fatehi, I., Amiri, B.J., Alizadeh, A., and Adamowski, J. Modeling the Relationship between Catchment Attributes and In-stream Water Quality Water Resources Management 29 (14) pp Foody, G.M.

Author: مریم حسین خواه, مهدی عرفانیان, احمد علیجانپور. Total Quality Management: Key Concepts and Case Studies provides the full range of management principles and practices that govern the quality function.

The book covers the fundamentals and background needed, as well as industry case studies and comprehensive topic coverage, making it an invaluable reference to both the novice and the more experienced individual. Air, Water, and Aquatic Environments (AWAE) Program - USDA Forest Service Science - RMRS The following maps illustrate some applications of the stream modeling approach using multiple regression.

Map Description Thumbnail of PDF: Modeled Stream Temperature. This book is open access under a CC BY-NC revised, updated textbook presents a systems approach to the planning, management, and operation of water resources infrastructure in the environment.

Previously published in by UNESCO and Deltares (Delft Hydraulics at the time). Although multiple linear regression models are an effective approach for identifying significant agricultural input intensity affecting water quality and explaining the relationship between agricultural land use intensity and stream water quality, they do not appear to quantitatively estimate contribution of respective agricultural land use intensity on the water quality because they are only based on the Cited by: Multiple linear regression models were developed to estimate microbial load in the raw water source, using data from the NRV drinking water treatment plant published from to and also from Norwegian school of veterinary science through VISK project.water-quality constituents of interest as well as water-quality conditions through time, potentially harmful cyanobacterial events, and changes in water-quality conditions that may affect drinking-water treatment processes.

Linear regression models for water-quality constituents. for the Kansas River at the Wamego and De Soto sites, includ-Author: Guy M. Foster, Jennifer L.

Graham.