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A new computing paradigm known as Web Services is quickly gathering momentum in companies ICT. Web Services represent an emerging model for developing and deploying software applications that promises to fundamentally change not only the way companies build and deploy software, but also how they communicate with their partners and customers. This approach can improve the flexibility and the reach of ecisting ICT infrastructures. An example from river-engineering and inundation management will be given.
The use of artificial neural networks for various hydrologic and hydromorphological studies is being investigated. Application of the Artificial Intelligence (AI) methods, especially the neural networks for water related studies is expanding lately, due to its advantages such as being less subjected to he constraints of the physical considerations and quick delivery of feasible and cost-effective responses. Primary stage results of the research are presented in two separate examples.The first example is the runoff modelling for the case of Yellowstone River, USA at the outlet of a high-altitude lake using neural networks, in which the influence of melting ice and snow cover is carefully considered. A wavelet transform is used for smoothening the input signal for the neural network model in order to improve the accuracy and prevent the inconsistency of neural network solutions. A morphological evolution study along cross-shore profiles at the Kiel Bay, Baltic Sea coast using ANN provides the second case. Data preprocessing or in this particular case downsampling of bathymetry measurements through a number of cross shore profiles was done by a wavelet transform.The data oriented approaches, such as neural networks often have to deal with the abundant data or noisy observations, which normally require a thorough analysis, preprocessing or downsampling, to enable a satisfactory performance of the models. The above case studies emphasize the importance and necessity of the data analysis and preprocessing.
The article reports an attempt to obtain a better generalization of neural network model by smoothening its input signal. The considered case is a river runoff modelling at the outlet of a high-altitude lake. For the test location, the dominant peak of the annual hydrograph is induced by snow melt in the late spring. The summer rainfall has a less significance to the streamflow and the variability of daily runoff is very low, due to a regulating effect of the lake. The ANN model derived the daily average runoff quite accurately, with small inconsistencies in the output, using only a few input variables. To improve the model performance, the time series of air temperature was smoothened by a wavelet transform and Savitzky-Golay algorithm to compare with the normal procedure of moving window averaging. After using the smoothened data by different techniques, a noticeable improvement has been attained in the quality of the output of the individual ANN models, while the accuracy of the solutions were comparable.
Modern urbanization tends to cause a fast response time between a heavy precipitation and consequent runoff processes in a river basin. Therefore, it is even more essential nowadays to enable citizens to have a rapid and flexible access to information on the prevention or restoration measures in cases of flood, on the basis of the cutting edge advances of the Information and Communication Technology (ICT). The paper contains a description of a real-time Web application and service for water level observation, processing, presentation and a short term prediction of a river water level in the area of interest. The Artificial Neural Networks (ANN) are implemented for cost-effective water level prediction for a short horizon. On the whole, the Web applications and services should form a part of a publicly accessible Web based flood crisis management system.