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Journal articles on the topic '090702 Environmental Engineering Modelling'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Hoti, Suhejla, Michael McAleer, and Laurent L. Pauwels. "Modelling environmental risk." Environmental Modelling & Software 20, no. 10 (October 2005): 1289–98. http://dx.doi.org/10.1016/j.envsoft.2004.08.010.

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2

Hobbs, S. E. "Modelling change in environmental systems." Applied Ocean Research 17, no. 5 (October 1995): 323. http://dx.doi.org/10.1016/0141-1187(95)90002-0.

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3

Schulin, Rainer. "Advances in Environmental Modelling (Developments in Environmental Modelling, Vol. 13)." Journal of Environmental Quality 19, no. 3 (July 1990): 631–32. http://dx.doi.org/10.2134/jeq1990.00472425001900030049x.

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4

Hamilton, E. I. "Advances in environmental modelling." Science of The Total Environment 92 (March 1990): 287–88. http://dx.doi.org/10.1016/0048-9697(90)90344-t.

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5

Rasinmäki, Jussi. "Modelling spatio-temporal environmental data." Environmental Modelling & Software 18, no. 10 (December 2003): 877–86. http://dx.doi.org/10.1016/s1364-8152(03)00106-3.

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6

McAleer, Michael. "Modelling economic and environmental systems." Environmental Modelling & Software 20, no. 11 (November 2005): 1365–67. http://dx.doi.org/10.1016/j.envsoft.2004.09.009.

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7

LUX, T., and W. MATTHEWS. "Advanced technology for environmental modelling." Environmental Modelling & Software 22, no. 3 (March 2007): 279–80. http://dx.doi.org/10.1016/j.envsoft.2005.07.027.

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8

Hutson, John L. "Modelling in Ecotoxicology (Developments in Environmental Modelling 16)." Journal of Environmental Quality 20, no. 4 (October 1991): 879–80. http://dx.doi.org/10.2134/jeq1991.00472425002000040035x.

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9

Janowski, Artur, Jakub Szulwic, and Paweł Tysiąc. "Spatial Modelling in Environmental Analysis and Civil Engineering." Applied Sciences 11, no. 9 (April 27, 2021): 3945. http://dx.doi.org/10.3390/app11093945.

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10

Wang, Julian Z. "Statistical modelling for extreme observations in environmental engineering." Environmetrics 9, no. 1 (January 1998): 31–36. http://dx.doi.org/10.1002/(sici)1099-095x(199801/02)9:1<31::aid-env283>3.0.co;2-d.

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11

Carvalho, I. S., P. Antão, and C. Guedes Soares. "Modelling of environmental impacts of ship dismantling." Ships and Offshore Structures 6, no. 1-2 (February 11, 2011): 161–73. http://dx.doi.org/10.1080/17445301003776233.

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12

Hamilton, E. I. "Receptor modelling in environmental chemistry." Science of The Total Environment 57 (December 1986): 269–70. http://dx.doi.org/10.1016/0048-9697(86)90036-7.

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13

O'Brien, R. S., P. McDonald, C. Yu, V. Koukouliou, L. W. Setlow, J. Horyna, D. P. Sanchéz, et al. "Environmental modelling of NORM." Radioprotection 44, no. 5 (2009): 23–28. http://dx.doi.org/10.1051/radiopro/20095011.

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14

Susanto, Ferry, Paulo de Souza, and Jing He. "Spatiotemporal Interpolation for Environmental Modelling." Sensors 16, no. 8 (August 6, 2016): 1245. http://dx.doi.org/10.3390/s16081245.

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15

Moore, R. V., and A. G. Hughes. "Integrated environmental modelling: achieving the vision." Geological Society, London, Special Publications 408, no. 1 (May 23, 2016): 17–34. http://dx.doi.org/10.1144/sp408.12.

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16

Stive, M. J. F. "Computer modelling in ocean engineering 91." Coastal Engineering 20, no. 1-2 (July 1993): 183. http://dx.doi.org/10.1016/0378-3839(93)90061-c.

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17

Speranza, N., B. Kidd, M. P. Schultz, and I. M. Viola. "Modelling of hull roughness." Ocean Engineering 174 (February 2019): 31–42. http://dx.doi.org/10.1016/j.oceaneng.2019.01.033.

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18

Roustan, M., C. Beck, O. Wable, J. P. Duguet, and J. Mallevialle. "Modelling Hydraulics of Ozone Contactors." Ozone: Science & Engineering 15, no. 3 (June 1993): 213–26. http://dx.doi.org/10.1080/01919519308552485.

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19

Benarie, Michel. "Integrated environmental modelling: Design and tools." Science of The Total Environment 71, no. 2 (May 1988): 236. http://dx.doi.org/10.1016/0048-9697(88)90173-8.

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20

Downing, Keith. "Using evolutionary computational techniques in environmental modelling." Environmental Modelling & Software 13, no. 5-6 (October 1998): 519–28. http://dx.doi.org/10.1016/s1364-8152(98)00050-4.

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21

Porter, N. "Modelling of Pollutants in Complex Environmental Systems." Environmental Modelling & Software 25, no. 6 (June 2010): 817. http://dx.doi.org/10.1016/j.envsoft.2009.12.005.

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22

Chen, Qiuwen, Yenory Morales-Chaves, Hong Li, and Arthur E. Mynett. "Hydroinformatics techniques in eco-environmental modelling and management." Journal of Hydroinformatics 8, no. 4 (December 1, 2006): 297–316. http://dx.doi.org/10.2166/hydro.2006.011.

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The high complexity of aquatic ecosystems and the multiple processes involved, make the development of ecohydraulics and eco-environmental models a challenging subject. Conventionally, computer-based models use a mathematical formulation for the processes involved which are then solved by numerical methods. These models are often derived based on the assumption of spatial homogeneity and conservation principles of mass, momentum and energy. Development of these models often demands a clear understanding of the processes involved. However, the above assumptions are easily violated when spatial heterogeneity, individual species behaviour and local interactions play a significant role in the system dynamics. In particular for eco-environmental systems, knowledge on local interactions that determine the overall system behaviour is not always available. Although the rapid advances of data-driven techniques have recently made great contributions to water-environment related research, data on ecosystems are often quite limited, which restricts the application of data mining methods to eco-environmental system modelling. In addition, no modelling – also not black-box modelling – can be undertaken without having at least some understanding of the basic processes and mechanisms involved. It is always advisable to start exploring any dataset using conventional statistical techniques, as elaborated in this paper for a case study on Western Xiamen Bay, China. Neural network trimming was then used to establish the dominant factors; it was shown that a relatively simple ANN model was quite capable of capturing the essential features, provided the right input parameters are chosen. Examples of integrated approaches to ecohydraulics modelling coupling formulations with cellular automata and physical equations with fuzzy rules are presented for applications on eutrophication modelling of Taihu Lake in China, competitive growths and colonization of two underwater macrophytes in Lake Veluwe in The Netherlands, and forecasting of algal blooms in the Dutch coastal waters on the North Sea. A mussel dynamics model developed for the Upper Mississippi River in the USA demonstrates the feasibility of individual based modelling in ecosystem dynamics. Numerical models are quite capable of simulating the abiotic aquatic environment, including complicated fluid flow and transport mechanisms. However, when it comes to simulating the biotic and ecosystem dynamics, the interaction of individual species with their environment, as well as the interactions amongst species, has to be taken into account. The future of ecohydraulics and eco-environmental modelling thus seems to lie in the integration of different paradigms and techniques, which is the core content of the hydroinformatics discipline.

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23

Jović, Jadranka, and Vladimir Đorić. "TRAFFIC AND ENVIRONMENTAL STREET NETWORK MODELLING: BELGRADE CASE STUDY." TRANSPORT 25, no. 2 (June 30, 2010): 155–62. http://dx.doi.org/10.3846/transport.2010.19.

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The paper presents a procedure for modelling transport demand and analyses effects caused by changes in the characteristics of street network exploitation. Based on the effects of the measures predominantly connected with speed restrictions on characteristic sections, the Scenarios resulting in reduced congestion during peak hour and a decrease in harmful effects of traffic were selected. A rough effectivity analysis of transport vehicle kilometres travelled and travel time on the network was carried out. Transportation modeling is the most efficient way to analyze the effects of traffic solutions. Once established, transport model brings only benefits in future projects. Speed reduction strategy was easily simulated using the transportation modeling procedure. Other sustainable transport and mobility management strategies considering sustainable transportation could also be simulated.

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24

Gerber, Marius. "Modelling dissipation in harbour resonance." Coastal Engineering 10, no. 3 (September 1986): 211–52. http://dx.doi.org/10.1016/0378-3839(86)90043-8.

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25

Marcot, Bruce G., and Trent D. Penman. "Advances in Bayesian network modelling: Integration of modelling technologies." Environmental Modelling & Software 111 (January 2019): 386–93. http://dx.doi.org/10.1016/j.envsoft.2018.09.016.

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26

Li, Qiuying, and Ruize Sun. "Environmental Engineering with Software Environment Modelling Based on Threat Model." IOP Conference Series: Earth and Environmental Science 706, no. 1 (March 1, 2021): 012020. http://dx.doi.org/10.1088/1755-1315/706/1/012020.

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27

Matei, Ion Viorel, and Laura Ungureanu. "SURVEY ON INTEGRATED MODELLING APPLIED IN ENVIRONMENTAL ENGINEERING AND MANAGEMENT." Environmental Engineering and Management Journal 13, no. 4 (2014): 1027–38. http://dx.doi.org/10.30638/eemj.2014.107.

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28

Souto González, J. A., P. M. Bello Bugallo, and J. J. Casares Long. "Evaluation of air pollution modelling tools as environmental engineering courseware." Water Science and Technology 49, no. 8 (April 1, 2004): 57–64. http://dx.doi.org/10.2166/wst.2004.0487.

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The study of phenomena related to the dispersion of pollutants usually takes advantage of the use of mathematical models based on the description of the different processes involved. This educational approach is especially important in air pollution dispersion, when the processes follow a non-linear behaviour so it is difficult to understand the relationships between inputs and outputs, and in a 3D context where it becomes hard to analyze alphanumeric results. In this work, three different software tools, as computer solvers for typical air pollution dispersion phenomena, are presented. Each software tool developed to be implemented on PCs, follows approaches that represent three generations of programming languages (Fortran77, VisualBasic and Java), applied over three different environments: MS-DOS, MS-Windows and the world wide web. The software tools were tested by students of environmental engineering (undergraduate) and chemical engineering (postgraduate), in order to evaluate the ability of these software tools to improve both theoretical and practical knowledge of the air pollution dispersion problem, and the impact of the different environment in the learning process in terms of content, ease of use and visualization of results.

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29

Carmo, José Simão Antunes do. "Numerical modelling and its physical modelling support in Civil Engineering." Research, Society and Development 9, no. 10 (October 5, 2020): e5019108409. http://dx.doi.org/10.33448/rsd-v9i10.8409.

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Currently, there is a progressive divestment of some institutions with strong traditions and skills in physical modelling and their consequent impoverishment, to the detriment of numerical modelling. For many reasons, the economic imperatives and the exponential growth of computational means and numerical methods should certainly not be excluded. In this work, the author aimed to highlight the new requirements of the recent sophisticated developments in physical modelling, precisely due to the new needs imposed on them by mathematical and numerical modelling and the growing risks in civil construction works. In this context, reflections are reported, justified by scientific and real-world examples, on the need for maintenance and reinforcement of investments in physical modelling, both to support the scientific community and to design buildings of significant economic, social and environmental impact.

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30

Almeida, José-Paulo de, and João Coutinho-Rodrigues. "Modelling environmental impacts over urban areas and facilities." Proceedings of the Institution of Civil Engineers - Municipal Engineer 164, no. 2 (June 2011): 103–16. http://dx.doi.org/10.1680/muen.2011.164.2.103.

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31

Ljunggren, Maria. "Modelling national solid waste management." Waste Management & Research 18, no. 6 (December 2000): 525–37. http://dx.doi.org/10.1177/0734242x0001800603.

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32

Jolma, A., A. Altartouri, and I. Ferencik. "GIS, Environmental Modeling and Engineering." Environmental Modelling & Software 25, no. 12 (December 2010): 1915–16. http://dx.doi.org/10.1016/j.envsoft.2010.06.007.

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33

Nour, Mohamed H., Daniel W. Smith, Mohamed Gamal El-Din, and Ellie E. Prepas. "Effect of watershed subdivision on water-phase phosphorus modelling: An artificial neural network modelling application." Journal of Environmental Engineering and Science 7, Supplement 1 (August 2008): 95–108. http://dx.doi.org/10.1139/s08-043.

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34

Wilkinson, P., and P. J. Dimbylow. "Radon diffusion modelling." Science of The Total Environment 45 (October 1985): 227–32. http://dx.doi.org/10.1016/0048-9697(85)90223-2.

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35

Hamilton, E. I. "Modelling in ecotoxicology." Science of The Total Environment 114 (April 1992): 293. http://dx.doi.org/10.1016/0048-9697(92)90445-x.

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36

Walcher, Sebastian, Joachim Altschuh, Karl-Werner Schramm, and Sebastian Mayer. "Estimates in deterministic fate modelling of environmental chemicals☆." Environmental Modelling & Software 18, no. 10 (December 2003): 929–36. http://dx.doi.org/10.1016/s1364-8152(03)00057-4.

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37

Krueger, Tobias, Trevor Page, Klaus Hubacek, Laurence Smith, and Kevin Hiscock. "The role of expert opinion in environmental modelling." Environmental Modelling & Software 36 (October 2012): 4–18. http://dx.doi.org/10.1016/j.envsoft.2012.01.011.

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38

Zhang, Chi, Yuan Li, Jinhai Zheng, Mingxiao Xie, Jian Shi, and Gang Wang. "Parametric modelling of nearshore wave reflection." Coastal Engineering 169 (October 2021): 103978. http://dx.doi.org/10.1016/j.coastaleng.2021.103978.

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39

Damgaard, Jesper, Nick Dodd, Lisa Hall, and Tim Chesher. "Morphodynamic modelling of rip channel growth." Coastal Engineering 45, no. 3-4 (May 2002): 199–221. http://dx.doi.org/10.1016/s0378-3839(02)00034-0.

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40

Sheng, Wanan, Raymond Alcorn, and Tony Lewis. "Physical modelling of wave energy converters." Ocean Engineering 84 (July 2014): 29–36. http://dx.doi.org/10.1016/j.oceaneng.2014.03.019.

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41

Lucas, Cláudia, and C. Guedes Soares. "On the modelling of swell spectra." Ocean Engineering 108 (November 2015): 749–59. http://dx.doi.org/10.1016/j.oceaneng.2015.08.017.

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42

De Vriend, H. J., and M. J. F. Stive. "Quasi-3D modelling of nearshore currents." Coastal Engineering 11, no. 5-6 (December 1987): 565–601. http://dx.doi.org/10.1016/0378-3839(87)90027-5.

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43

de Vriend, H. J., J. Zyserman, J. Nicholson, J. A. Roelvink, P. Péchon, and H. N. Southgate. "Medium-term 2DH coastal area modelling." Coastal Engineering 21, no. 1-3 (December 1993): 193–224. http://dx.doi.org/10.1016/0378-3839(93)90050-i.

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44

Hudson, Justin, Jesper Damgaard, Nick Dodd, Tim Chesher, and Alan Cooper. "Numerical approaches for 1D morphodynamic modelling." Coastal Engineering 52, no. 8 (August 2005): 691–707. http://dx.doi.org/10.1016/j.coastaleng.2005.04.004.

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45

Johnson, D., and C. Pattiaratchi. "Boussinesq modelling of transient rip currents." Coastal Engineering 53, no. 5-6 (April 2006): 419–39. http://dx.doi.org/10.1016/j.coastaleng.2005.11.005.

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46

Jamal, M. H., D. J. Simmonds, and V. Magar. "Modelling gravel beach dynamics with XBeach." Coastal Engineering 89 (July 2014): 20–29. http://dx.doi.org/10.1016/j.coastaleng.2014.03.006.

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47

Segarceanu, Svetlana, George Suciu, and Inge Gavăt. "Environmental Acoustics Modelling Techniques for Forest Monitoring." Advances in Science, Technology and Engineering Systems Journal 6, no. 3 (May 2021): 15–26. http://dx.doi.org/10.25046/aj060303.

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48

Fell, David, Jayne Cox, and David C. Wilson. "Future waste growth, modelling and decoupling." Waste Management & Research 28, no. 3 (February 2, 2010): 281–86. http://dx.doi.org/10.1177/0734242x10361512.

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49

Humphries, Michael Peter. "Modelling Congestion At Refuse Reception Installations." Waste Management & Research 4, no. 1 (January 1986): 279–91. http://dx.doi.org/10.1177/0734242x8600400134.

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50

Beck, M. B. "Principles of Modelling." Water Science and Technology 24, no. 6 (September 1, 1991): 1–8. http://dx.doi.org/10.2166/wst.1991.0134.

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The scope for modelling the behaviour of pollutants in the aquatic environment is now immense. In many practical applications there are effectively no computational constraints on what is possible. There is accordingly an increasing need for a set of principles of modelling that in some respects may well be different from those applicable when conceptualisation, the accuracy of the numerical solution scheme, and the inadequacies of an overly simplified model structure, were the issues of the day. Given the availability of increasingly comprehensive software, the user of a model is increasingly likely to be accelerated into a position where the issue of model calibration (identification) is an immediate problem. From the practical point of view of needing to make a decision on the control of a pollutant, the problem of identification may, or may not, be avoided. It is argued that a consistent approach to establishing whether such identification is necessary depends on establishing the significance, or otherwise, of model uncertainty. Identifying the model against field data does not have merely the goal of yielding “best” estimates of the unknown coefficients (parameters) appearing in the given model structure. It may also serve the purpose of identifying and modifying the uncertainty attaching to the model as a description of observed behaviour, which uncertainty will then be propagated forward in any predictions made with the model.

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