- Time series modelling of water resources and environmental systems
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- Time series modelling of water resources and environmental systems - Semantic Scholar
Date: May 28, Date: July 3, Date: May 23, Czerwinski, Remedios Cabrera. Date: July 22, Date: November 4, Why Us? All Rights Reserved.
- Time Series Modelling of Water Resources and Environmental Systems;
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Coastal hydraulics was also the subject of the area. Mathematical modeling was another nucleus of research interest, since general solutions to the problems related to the works described above can be obtained through the careful application of this tool, based on concepts of Computational Fluid Mechanics, non-permanent flow in channels And turbulence models. In this context, the impacts of dam rupture and computational modeling of estuaries and bays were concrete problems addressed in this area.
The second research approach, associated to the former Hydrological Engineering field, can be conceptualized as the set of methodologies, models and concepts that aim at the application of the principles of hydrology in the solution of practical engineering problems. Thus, the main research activities were based on activities related to hydrometry, flow and precipitation forecasting, remote sensing of hydrological variables, reservoir operation, hydrological regionalization, definition of design parameters for hydraulic works, analysis of risks associated with floods And droughts, sediment transport and other contaminants, and also the effects of anthropic changes on the hydrological cycle.
Hydrological engineering is part of the engineering of water resources, this is understood as the application of engineering in the use of water for the present and future well-being of man. One of the most striking aspects of hydrological engineering is its essentially quantitative approach, a common feature of all engineers seeking to establish a cause-and-effect relationship not only to identify the variables that are influenced by a given perturbation in a system but, The magnitude of the change in each of the variables.
For the development of techniques aimed at solving these problems, in addition to the hydrology discipline itself, a solid training in statistics, economics, fluid mechanics and, especially, given their quantitative character in mathematics, were essential. The quantitative aspect is that it distinguishes these research activities from other correlates, however, of a more descriptive nature, such as ecology, hydrogeography, water law, natural resource management and policy, and environmental education.
It is also important to emphasize the utilitarian nature of hydrological engineering, centered on the solution of practical problems, and its development is an example of applied research.
Time series modelling of water resources and environmental systems
A study of the causes of the abnormally high levels of Lake Malawi. Lilongwe: Wat. Variation in the level of Lake Malawi. GoM Comparison between traditional methods and artificial neural networks for ammonia concentration forecasting in an eel Anguilla Anguilla L. Intensive rearing system.
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- Simulation and analysis of temporal changes of groundwater depth using time series modeling.
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Hipel K, McLeod A Amsterdam: Elsevier IBM Corp Johnson T, Davis T High resolution profiles from Lake Malawi, Africa. Kantz H, Schreiber T Nonlinear Time Series Analysis 2nd Edn. Cambridge: Cambridge University Press.
Kaunda P Kidd C A water resources evaluation of Lake Malawi and the Shire River. Kumambala P, Ervine A Kumambala P Sustainability of water resources development for Malawi with particular emphasis on North and Central Malawi. PhD thesis. Glasgrow, UK: University of Glasgrow. Lazaro M, Jere W Lombardo R, Flaherty J Ljung G, Box G On a measure of lack of fit in time series models.
Resea 5 09 Neuland H Abnormal high water levels of Lake Malawi? Sankar I Stochastic Modelling Approach for forecasting fish product export in Tamilnadu. Scholz E, Rozendahl B Shela O Resea Stuffer D, Dhumway R Time series Analysis and its Application 3rd Ed. New Yolk: Springer. Yevjevich V Probability and Statistics in Hydrology. Colorado: Water Resources Pub.
Zhang G A neural network ensemble method with jittered training data for time series forecasting. This article is published under the terms of the Creative Commons Attribution License 4. Reviewers Reviewers Guidelines. Editors Editors.
News All News. Conferences All Conferences. Water Res. Ishmael B. Chikumbusko C. Article Number - D80C Vol. Lake Malawi has been there over the years. The stationarity of a stochastic process was visualized in form of a data plot as shown in Figure 2.
Time series modelling of water resources and environmental systems - Semantic Scholar
The basic model verification is concerned with checking the residues to see if they contain any systematic pattern which could still be eliminated to improve the performance of the selected model. Back to Vol. Back to articles. Kosamu Chikumbusko C. Chatfield C Model uncertainty and forecast accuracy. Forecasting pp.