Relevant bibliographies by topics / Water quality modelling

Academic literature on the topic 'Water quality modelling'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 January 2023

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Journal articles on the topic "Water quality modelling"

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Wood, R. G. "Water quality modelling." Marine Environmental Research 37, no. 3 (January 1994): 329–30. http://dx.doi.org/10.1016/0141-1136(94)90058-2.

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Kompare, Boris. "Water quality modelling." Ecological Modelling 72, no. 1-2 (March 1994): 145–49. http://dx.doi.org/10.1016/0304-3800(94)90149-x.

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van Griensven, A., and W. Bauwens. "Integral water quality modelling of catchments." Water Science and Technology 43, no. 7 (April 1, 2001): 321–28. http://dx.doi.org/10.2166/wst.2001.0441.

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ESWAT - Extended Soil and Water Assessment Tool - was developed to allow for an integral modelling of the water quantity and quality processes in river basins. The diffuse pollution sources are assessed by considering crop and soil processes and - together with the point sources - further transformed by an in-stream water quality module. An autocalibration procedure allows for the optimisation of the process parameters. The optimisation uses a global optimisation criterion, whereby several objective functions can be considered or combined and whereby several output variables can be taken into account simultaneously. The model and the calibration procedure are applied to the river Dender in Belgium.
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Seyoum, Alemtsehay G., and Tiku T. Tanyimboh. "Pressure-dependent network water quality modelling." Proceedings of the Institution of Civil Engineers - Water Management 167, no. 6 (June 2014): 342–55. http://dx.doi.org/10.1680/wama.12.00118.

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Høybye, Jan A. "Uncertainty Analysis in Water Quality Modelling." Hydrology Research 27, no. 3 (June 1, 1996): 203–14. http://dx.doi.org/10.2166/nh.1996.0005.

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An important part of regional planning of water resources and quality is efficient design of monitoring systems and proper use of hydrologic models (Beven 1993). In the design of monitoring systems as well as validation of numerical models, based on, for example, the equation of continuity such as hydrologic routing models and mass balance nutrient models, it is essential to estimate the uncertainties of the model-predictions. This paper presents an implementation of a first-order analysis for estimating the error-propagation when introducing mass balance models as to predict nutrient-concentrations. The uncertainty assessment, developed from a first order theory, is implemented in the analysis and modelling of Hjarbaek fjord in Denmark. The project includes hydrological modelling of input of water and nutrients to the fjord from tributaries, and a hydrodynamic estimation of water levels and velocities in the fjord. A two-system water quality box-model is used for estimation of concentrations in water and sediment phases. The system uncertainties are analysed, starting with input data uncertainties and the error propagation to the final concentration estimates, in order to optimise the future monitoring programme, and to control the model results.
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Margeta, J., and I. Fistanic. "Water quality modelling of Jadro spring." Water Science and Technology 50, no. 11 (December 1, 2004): 59–66. http://dx.doi.org/10.2166/wst.2004.0671.

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Management of water quality in karst is a specific problem. Water generally moves very fast by infiltration processes but far more by concentrated flows through fissures and openings in karst. This enables the entire surface pollution to be transferred fast and without filtration into groundwater springs. A typical example is the Jadro spring. Changes in water quality at the spring are sudden, but short. Turbidity as a major water quality problem for the karst springs regularly exceeds allowable standards. Former practice in problem solving has been reduced to intensive water disinfection in periods of great turbidity without analyses of disinfection by-products risks for water users. The main prerequisite for water quality control and an optimization of water disinfection is the knowledge of raw water quality and nature of occurrence. The analysis of monitoring data and their functional relationship with hydrological parameters enables establishment of a stochastic model that will help obtain better information on turbidity in different periods of the year. Using the model a great number of average monthly and extreme daily values are generated. By statistical analyses of these data possibility of occurrence of high turbidity in certain months is obtained. This information can be used for designing expert system for water quality management of karst springs. Thus, the time series model becomes a valuable tool in management of drinking water quality of the Jadro spring.
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Câmara, A. S., M. J. Seixas, M. D. Pinheiro, and M. P. Antunes. "“GLOBAL” Modelling for Water Quality Planning." IFAC Proceedings Volumes 18, no. 14 (October 1985): 87–91. http://dx.doi.org/10.1016/s1474-6670(17)60039-9.

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Jaffé, Peter R. "An introduction to water quality modelling." Advances in Water Resources 9, no. 2 (June 1986): 108. http://dx.doi.org/10.1016/0309-1708(86)90019-9.

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Cebe, Kağan, and Lale Balas. "Water quality modelling in kaş bay." Applied Mathematical Modelling 40, no. 3 (February 2016): 1887–913. http://dx.doi.org/10.1016/j.apm.2015.09.037.

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Kumar, Amit, Santosh Subhash Palmate, and Rituraj Shukla. "Water Quality Modelling, Monitoring, and Mitigation." Applied Sciences 12, no. 22 (November 10, 2022): 11403. http://dx.doi.org/10.3390/app122211403.

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In the modern era, water quality indices and models have received attention from environmentalists, policymakers, governments, stakeholders, water resource planners, and managers for their ability to evaluate the water quality of freshwater bodies. Due to their wide applicability, models are generally developed based on site-specific guidelines and are not generic; therefore, predicted/calculated values are reported to be highly uncertain. Thus, model and/or index formulation are still challenging and represent a current research hotspot in the scientific community. The inspiration for this Special Issue came from our desire to provide a platform for sharing results and informing young minds around the world to develop suitable models to understand water quality so that mitigation measures can be taken in advance to make water fit for drinking and for life-supporting activities.
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Dissertations / Theses on the topic "Water quality modelling"

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Maier, Stefan Heinrich. "Modelling water quality for water distribution systems." Thesis, Brunel University, 1999. http://bura.brunel.ac.uk/handle/2438/5431.

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Maintaining water quality in distribution systems has become a prominent issue in the study of water networks. This thesis concentrates on disinfectant and particle counts as two important indicators of water quality. The models discussed in this work are based on data collected by the author. The experimental set-up and procedure are described and observations of particle counts, particle counter size distributions, monochloramine as disinfectant, temperature, heterotrophic plate counts and epifluorescence microscopy counts are reported. A model of the response of particle counts to an increase in flow is developed. This model is obtained from specification derived from the data and assumptions, and is validated by its interpretability and its fit to data. A local shear-off density and an initial biofilm shedding profile were introduced and thus a linear model for this part of the water quality dynamics could be obtained. A procedure for the identification of the parameters of the local shear-off function and for the determination of the biofilm shedding profile is presented. This profile can be used to provide information about the status of the distribution system in terms of shear-off from the biofilm on the pipe walls. Monochloramine decay dynamics are investigated. The chlorine meter data is preprocessed with the help of titration data to correct meter drift. The data is then used in calibrating two different possible chlorine models: a model with a single decay coefficient and a model with bulk decay coefficient and wall demand (as used in Epanet). Important difficulties in identifying these parameters that come about because of the structure of the models are highlighted. Identified decay coefficients are compared and tested for flow, inlet chlorine and temperature dependence. The merits and limits of the approach to modelling taken in this work and a possible generalisation are discussed. The water industry perspective and an outlook are provided.
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Machell, John. "Water quality modelling in distribution networks." Thesis, University of Sheffield, 2004. http://etheses.whiterose.ac.uk/14849/.

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The thesis is a treatise of the quantity and quality aspects of potable water in distribution systems. The privatisation of the UK Water Industry in 1989 has seen the requirement for the Water Companies in England and Wales to be responsible for the delivery of good quality water that meets the demand of all consumers. In respect of the quantity of supply, there have been many previous studies that have examined the hydraulic performance of distribution systems and there are now many proprietary mathematical models that have been successfully used in this study. However, in respect of water quality the literature review has highlighted that the modelling approach is not so well advanced, as water quality is a function of many concepts, processes and parameters that include the source and age of water, the condition and deterioration of the assets in the system, the microbiological, chemical and physical processes and the network hydraulic performance, including pressure transients. These processes are highly interactive and complex. In an attempt to better understand these processes a programme of research has been completed that has involved a field evaluation of the performance of a live system, including the development of instrumentation to continually measure water quality, and the development of a mathematical model to describe the processes associated with the age of water and the propagation of conservative and non-conservative substances. An initial attempt has also been made to develop a micro-biological model and a sediment transport model. New original concepts developed by the author include age, biological and diagnostic models that may be used to identify the source of any incident (hydraulic or pollution) and the application of the model in near real time.
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Jacobs, Haden. "Modelling water quality : complexity versus simplicity." Thesis, Rhodes University, 2017. http://hdl.handle.net/10962/4754.

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Water quality management makes use of water quality models as decision making tools. Water quality management decisions need to be informed by information that is as reliable as possible. There are many situations where observational data are limited and therefore models or simulation methods have a significant role to play in providing some information that can be used to guide management decisions. Water quality modelling is the use of mathematical equations and statistics to represent the processes affecting water quality in the natural environment. Water quality data are expensive and difficult to obtain. Nutrient sampling requires a technician to obtain ‘grab samples’ which need to be kept at low temperatures and analysed in a laboratory. The laboratory analyses of nutrients is expensive and time consuming. The data required by water quality models are seldom available as complete datasets of sufficient length. This is especially true for ungauged regions, either in small rural catchments or even major rivers in developing countries. Water quality modelling requires simulated or observed water quantity data as water quality is affected by water quantity. Both the water quality modelling and water quantity modelling require data to simulate the required processes. Data are necessary for both model structure as well as model set up for calibration and validation. This study aimed to investigate the simulation of water quality in a low order stream with limited observed data using a relatively complex as well as a much simpler water quality model, represented by QUAL2K and an in-house developed Mass Balance Nutrient (MBN) model, respectively. The two models differ greatly in the approach adopted for water quality modelling, with QUAL2K being an instream water quality fate model and the MBN model being a catchment scale model that links water quantity and quality. The MBN model uses hydrological routines to simulate those components of the hydrological cycle that are expected to differ with respect to their water quality signatures (low flows, high flows, etc.). Incremental flows are broken down into flow fractions, and nutrient signatures are assigned to fractions to represent catchment nutrient load input. A linear regression linked to an urban runoff model was used to simulate water quality entering the river system from failing municipal infrastructure, which was found to be a highly variable source of nutrients within the system. A simple algal model was adapted from CE-QUAL-W2 to simulate nutrient assimilation by benthic algae. QUAL2K, an instream water quality fate model, proved unsuitable for modelling diffuse sources for a wide range of conditions and was data intensive when compared to the data requirements of the MBN model. QUAL2K did not simulate water quality accurately over a wide range of flow conditions and was found to be more suitable to simulating point sources. The MBN model did not provide accurate results in terms of the simulation of individual daily water quality values; however, the general trends and frequency characteristics of the simulations were satisfactory. Despite some uncertainties, the MBN model remains useful for extending data for catchments with limited observed water quality data. The MBN model was found to be more suitable for South African conditions than QUAL2K, given the data requirements of each model and water quality and flow data available from the Department of Water and Sanitation. The MBN model was found to be particularly useful by providing frequency distributions of water quality loads or concentrations using minimal data that can be related to the risks of exceeding management thresholds.
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Braga, Maria Cristina Borba. "Surface water quality modelling of mercury contamination." Thesis, Imperial College London, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.396307.

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Sherwood, Julia M. "Modelling minewater flow and quality changes after coalfield closure." Boston Spa, U.K. : British Library Document Supply Centre, 1997. http://ethos.bl.uk/OrderDetails.do?did=1&uin=uk.bl.ethos.363536.

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McIntyre, Neil Robert. "Analysis of uncertainty in river water quality modelling." Thesis, Imperial College London, 2004. http://hdl.handle.net/10044/1/11828.

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Sincock, Andrew Michael. "Conceptual river water quality modelling under dynamic conditions." Thesis, Imperial College London, 2002. http://hdl.handle.net/10044/1/11912.

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Newham, Lachlan Thomas Hopkins, and lachlan newham@anu edu au. "Catchment Scale Modelling of Water Quality and Quantity." The Australian National University. Centre for Resource and Environmental Studies, 2002. http://thesis.anu.edu.au./public/adt-ANU20050919.144548.

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Appropriately constructed pollutant export models can help set management priorities for catchments, identify critical pollutant source areas, and are important tools for developing and evaluating economically viable ways of minimising surface water pollution.¶ This thesis presents a comparison, an evaluation and an integration of models for predicting the export of environmental pollutants, in particular sediment, through river systems. A review of the capabilities and limitations of current water quality modelling approaches is made. Several water quality and quantity modelling approaches are applied and evaluated in the catchment of the upper Murrumbidgee River.¶ The IHACRES rainfall-runoff model and a simple hydrologic routing model are applied with the aim of developing a capacity to predict streamflow at various catchment scales and to enable integration with other pollutant load estimation techniques. Methods for calculating pollutant loads from observed pollutant concentration and modelled streamflow data are also investigated. Sediment export is estimated using these methods over a 10-year period for two case study subcatchments. Approaches for water quality sampling are discussed and a novel monitoring program using rising stage siphon samplers is presented. Results from a refinement of the Sediment River Network model in the upper Murrumbidgee catchment (SedNet-UM) are presented. The model provides a capacity to quantify sediment source, transport and to simulate the effects of management change in the catchment. The investigation of the model includes rigorous examination of the behaviour of the model through sensitivity assessment and comparison with other sediment modelling studies. The major conclusion reached through sensitivity assessment was that the outputs of the model are most sensitive to perturbation of the hydrologic parameters of the model.¶ The SedNet-UM application demonstrates that it is possible to construct stream pollutant models that assist in prioritising management across catchment scales. It can be concluded that SedNet and similar variants have much potential to address common resource management issues requiring the identification of the source, propagation and fate of environmental pollutants. In addition, incorporating the strengths of a conceptual rainfall-runoff model and the semi-distributed SedNet model has been identified as very useful for the future prediction of environmental pollutant export.
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Kamish, Wageed. "Hydrosalinity modelling of the Berg River using ACRUSalanity /." Link to the online version, 2008. http://hdl.handle.net/10019/2022.

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Goodhue, Nigel David. "Hydrodynamic and water quality modelling of the lower Kaituna River and Maketu Estuary /." The University of Waikato, 2007. http://hdl.handle.net/10289/2375.

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The Maketu Estuary is a shallow intertidal estuary (2.3 km2) located in the Bay of Plenty, North Island, New Zealand. The Kaituna River contributes the largest freshwater flow into the estuary through control gates. Lake Rotoiti and indirectly Lake Rotorua supply the base flow to the Kaituna River, with tributaries along the 50 km reach also significantly contributing to the flow. Water quality within the river is affected by elevated nutrients, faecal coliforms, high oxygen demand and algae concentrations derived from the lakes as well as contributions from tributaries and industrial and urban discharge. Through the use of a coupled hydrodynamic-biogeochemical numerical model ELCOM-CAEDYM, this study aims to examine the nutrient, phytoplankton and hydrodynamics of the Maketu Estuary and lower Kaituna River. Water quality and hydrodynamic measurements were sourced from Environment Bay of Plenty's data archives as well as a number of instrument deployments to collect water velocity, tidal elevation and salinity and temperature measurements during the course of this study. Included in the field work was a survey of the lower river and estuary bathymetry. Model simulations predicted that the maximum residence time in the Maketu Estuary is 1.5 days, occurring in the inner western region. Residence time in the lower river (mouth to 8.5 km upstream) is in the order of hours although some variations were predicted near the river mouth. Growth rates of four phytoplankton groups where assessed over a 15 day period in January 2004. In the Kaituna River ELCOM-CAEDYM predicted that the community growth rates were small with the exception of a slight increase in biomass of the two freshwater groups in a semi-detached river bend. The increase in the loop was correlated with an increase of residence time. In the estuary, marine diatoms showed the highest growth rates in the western region which is expected to relate to retention time and available nutrients. Dinoflagellates showed the smallest variation in predicted growth rates, most likely due to their broad salinity tolerance. The two freshwater species showed a reduction in abundance when mixed with marine water. A principle limiting factor to phytoplankton growth in both the river and estuary is the low residence time. A number of scenarios were simulated in the river and estuary by altering the forcing conditions in the model. A simulation of the increased nutrient load associated with the Rotoiti diversion wall revealed that phytoplankton growth in the river and estuary will not be significantly affected. Because of the close proximity of the control gates to the river mouth, a proportion of water drawn through the structure can be marine. By opening the old river channel, model simulations predicted that a reduction in salinity would be possible, however the outcome of complete freshwater is probably not achievable. Increasing the discharge volume from the river into the estuary was also simulated. The results indicated that increasing the freshwater inflow at Fords Cut would reduce the salinity in the estuary while increasing the net (residual) flow towards the estuary mouth. Increasing the flow would also result in a greater range of salinity in regions of the estuary. Changing the inflow location to the historic Papahikahawai Channel also affected the salinity in the estuary. The most significant effect of an inflow at this location was a reduction of the residual currents in the western region of the estuary.
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Books on the topic "Water quality modelling"

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National Symposium on Hydrology (India) (11th 2004 Roorkee, India). Water quality: Monitoring, modelling, and prediction. Edited by Jain C. K, Trivedi R. C, Sharma K. D, National Institute of Hydrology (India), and India. Central Pollution Control Board. New Delhi: Allied Publishers, 2004.

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Wu, Wenyan. Water quality modelling and calibration in water distribution systems. Leicester: De Montfort University, 1998.

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Benedini, Marcello. Water Quality Modelling for Rivers and Streams. Dordrecht: Springer Netherlands, 2013.

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Benedini, Marcello, and George Tsakiris. Water Quality Modelling for Rivers and Streams. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-5509-3.

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Vanrolleghem, Peter A. Modelling aspects of water framework directive implementation. London: IWA Pub., 2010.

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Joshi, Vijay. Surface water quality modelling: State of art in India. Roorkee: INCOH Secretariat, 1997.

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South Saskatchewan regional plan: Water quantity and quality modelling results. [Edmonton]: Alberta Environment, 2010.

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Michaelis, W., ed. Estuarine Water Quality Management Monitoring, Modelling and Research. Washington, D. C.: American Geophysical Union, 1990. http://dx.doi.org/10.1029/ce036.

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Perk, Marcel van der. Muddy waters: Uncertainty issues in modelling the influence of bed sediments on water composition. Amsterdam: Koninklijk Nederlands Aardrijkskundig Genootschap, 1996.

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1952-, Wrobel L. C., and Latinopoulos P, eds. Water pollution III: Modelling, measuring, and prediction. Southampton: Computational Mechanics Publications, 1995.

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Book chapters on the topic "Water quality modelling"

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Říha, Jaromír. "Stream Water Quality Modelling Techniques." In Springer Water, 107–31. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18359-2_5.

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Warn, A. E. "River Quality Modelling." In Surface Water Pollution and its Control, 322–43. London: Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-09071-6_14.

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Parlange, J. Yves, Tammo S. Steenhuis, Frank Stagnitti, Erik Simmelink, and Bart Nijssen. "Recent Advances in Modelling Vadose Zone Transport." In Water-Quality Hydrology, 127–51. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-011-0393-0_9.

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Sivakumar, M., and S. Boroumand-Nasab. "Nutrient runoff modelling of an Australian urban catchment." In Water-Quality Hydrology, 81–90. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-011-0393-0_6.

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Müller, A., M. Grodd, and H. P. Weigel. "Lower Weser Monitoring and Modelling." In Estuarine Water Quality Management, 285–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-75413-5_43.

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Gnauck, Albrecht. "Time Series Analysis of Water Quality Data." In Process Modelling, 509–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60120-0_35.

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Leussen, Wim, and Job Dronkers. "Progress in Estuarine Water Quality Modelling." In Estuarine Water Quality Management, 13–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-75413-5_2.

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Bobba, A. Ghosh, and Vijay P. Singh. "Application of Monte Carlo Analysis to Saturated Subsurface Contamination Modelling." In Water-Quality Hydrology, 197–213. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-011-0393-0_13.

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Murphy, John F., and John Davy-Bowker. "The Predictive Modelling Approach to Biomonitoring: Taking River Quality Assessment Forward." In Water Quality Measurements, 383–99. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470863781.ch20.

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Krohn, Joachim, and Kurt C. Duwe. "Mathematical Modelling of Hydrodynamics in the Elbe Estuary." In Estuarine Water Quality Management, 35–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-75413-5_4.

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Conference papers on the topic "Water quality modelling"

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"Conceptual modelling for water quality management." In 22nd International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2017. http://dx.doi.org/10.36334/modsim.2017.l21.fu.

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"Future Water: Comparing and contrasting approaches to predicting water quality." In 23rd International Congress on Modelling and Simulation (MODSIM2019). Modelling and Simulation Society of Australia and New Zealand, 2019. http://dx.doi.org/10.36334/modsim.2019.k15.guo.

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Tomperi, Jani, Esko Juuso, and Kauko Leiviska. "Water Quality Modelling and Control in a Water Treatment Process." In 2013 8th EUROSIM Congress on Modelling and Simulation (EUROSIM). IEEE, 2013. http://dx.doi.org/10.1109/eurosim.2013.31.

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"Catchment modelling scenarios to inform GBR water quality targets." In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.l21.waters.

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Zahari, Nazirul Mubin, Mohd Hafiz Zawawi, Zakaria Che Muda, Lariyah Mohd Sidek, Nurfazila Mohd Fauzi, Mohd Edzham Fareez Othman, and Zulkepply Ahmad. "Klang River water quality modelling using music." In 3RD ELECTRONIC AND GREEN MATERIALS INTERNATIONAL CONFERENCE 2017 (EGM 2017). Author(s), 2017. http://dx.doi.org/10.1063/1.5002326.

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"Assessment of water quality in Hawkesbury-Nepean River in Sydney using water quality index and multivariate analysis." In 21st International Congress on Modelling and Simulation (MODSIM2015). Modelling and Simulation Society of Australia and New Zealand, 2015. http://dx.doi.org/10.36334/modsim.2015.l16.haque.

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"Determining the impact of reservoir water transfers on water quality using advanced methods." In 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.i5.fornarelli.

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"Suitability of a coupled hydrodynamic water quality model to predict changes in water quality from altered meteorological boundary conditions." In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.l5.vanderlinden.

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Widyastuti, Indri, and Suntoyo. "Modelling Water Quality in Welang River Estuary, Pasuruan." In The 7th International Seminar on Ocean and Coastal Engineering, Environmental and Natural Disaster Management. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0010218702750280.

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Widyastuti, Indri, and Suntoyo. "Modelling Water Quality in Welang River Estuary, Pasuruan." In The 7th International Seminar on Ocean and Coastal Engineering, Environmental and Natural Disaster Management. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0010218702510256.

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Reports on the topic "Water quality modelling"

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Mainali, Janardan. Spatial Statistical Approaches to Water Quality Modelling. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.7383.

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Daudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe, and Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, December 2021. http://dx.doi.org/10.53328/uxuo4751.

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The report provides a review of how risk is conceived of, modelled, and mapped in studies of infectious water, sanitation, and hygiene (WASH) related diseases. It focuses on spatial epidemiology of cholera, malaria and dengue to offer recommendations for the field of WASH-related disease risk mapping. The report notes a lack of consensus on the definition of disease risk in the literature, which limits the interpretability of the resulting analyses and could affect the quality of the design and direction of public health interventions. In addition, existing risk frameworks that consider disease incidence separately from community vulnerability have conceptual overlap in their components and conflate the probability and severity of disease risk into a single component. The report identifies four methods used to develop risk maps, i) observational, ii) index-based, iii) associative modelling and iv) mechanistic modelling. Observational methods are limited by a lack of historical data sets and their assumption that historical outcomes are representative of current and future risks. The more general index-based methods offer a highly flexible approach based on observed and modelled risks and can be used for partially qualitative or difficult-to-measure indicators, such as socioeconomic vulnerability. For multidimensional risk measures, indices representing different dimensions can be aggregated to form a composite index or be considered jointly without aggregation. The latter approach can distinguish between different types of disease risk such as outbreaks of high frequency/low intensity and low frequency/high intensity. Associative models, including machine learning and artificial intelligence (AI), are commonly used to measure current risk, future risk (short-term for early warning systems) or risk in areas with low data availability, but concerns about bias, privacy, trust, and accountability in algorithms can limit their application. In addition, they typically do not account for gender and demographic variables that allow risk analyses for different vulnerable groups. As an alternative, mechanistic models can be used for similar purposes as well as to create spatial measures of disease transmission efficiency or to model risk outcomes from hypothetical scenarios. Mechanistic models, however, are limited by their inability to capture locally specific transmission dynamics. The report recommends that future WASH-related disease risk mapping research: - Conceptualise risk as a function of the probability and severity of a disease risk event. Probability and severity can be disaggregated into sub-components. For outbreak-prone diseases, probability can be represented by a likelihood component while severity can be disaggregated into transmission and sensitivity sub-components, where sensitivity represents factors affecting health and socioeconomic outcomes of infection. -Employ jointly considered unaggregated indices to map multidimensional risk. Individual indices representing multiple dimensions of risk should be developed using a range of methods to take advantage of their relative strengths. -Develop and apply collaborative approaches with public health officials, development organizations and relevant stakeholders to identify appropriate interventions and priority levels for different types of risk, while ensuring the needs and values of users are met in an ethical and socially responsible manner. -Enhance identification of vulnerable populations by further disaggregating risk estimates and accounting for demographic and behavioural variables and using novel data sources such as big data and citizen science. This review is the first to focus solely on WASH-related disease risk mapping and modelling. The recommendations can be used as a guide for developing spatial epidemiology models in tandem with public health officials and to help detect and develop tailored responses to WASH-related disease outbreaks that meet the needs of vulnerable populations. The report’s main target audience is modellers, public health authorities and partners responsible for co-designing and implementing multi-sectoral health interventions, with a particular emphasis on facilitating the integration of health and WASH services delivery contributing to Sustainable Development Goals (SDG) 3 (good health and well-being) and 6 (clean water and sanitation).
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