Using oceanic-atmospheric oscillations for long lead time streamflow forecasting

We present a data-driven model, Support Vector Machine (SVM), for long lead time streamflow forecasting using oceanic-atmospheric oscillations. The SVM is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach and has been used to predict a quantity forward in time on the basis of training from past data. The strength of SVM lies in minimizing the empirical classification error and maximizing the geometric margin by solving inverse problem. The SVM model is applied to three gages, i.e., Cisco, Green River, and Lees Ferry in the Upper Colorado River Basin in the western United States. Annual oceanic-atmospheric indices, comprising Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and El Nino–Southern Oscillations (ENSO) for a period of 1906–2001 are used to generate annual streamflow volumes with 3 years lead time. The SVM model is trained with 86 years of data (1906–1991) and tested with 10 years of data (1992–2001). On the basis of correlation coefficient, root means square error, and Nash Sutcliffe Efficiency Coefficient the model shows satisfactory results, and the predictions are in good agreement with measured streamflow volumes. Sensitivity analysis, performed to evaluate the effect of individual and coupled oscillations, reveals a strong signal for ENSO and NAO indices as compared to PDO and AMO indices for the long lead time streamflow forecast. Streamflow predictions from the SVM model are found to be better when compared with the predictions obtained from feedforward back propagation artificial neural network model and linear regression

Generation and nomenclature of tessellations and double-layer grids

The aim of this work is to establish a systematic methodology for generating automatically different tessellations and double-layer grids (DLGs) following a defined and specific nomenclature proposed originally for such a task. This particular nomenclature defines the notation of mosaics and DLGs in a synthesized and unique manner, with the advantage that it shows how to generate and design them after the parameters expressed on their own names. As a result, by means of an algorithm and some computational codes, it is possible to recreate in 3D any of those grids directly from their own names. Current nomenclature for tessellations is also analyzed, finding severe disadvantages, such as the excessive length of their notations or their non-uniqueness character. A new nomenclature is proposed in order to define and generate consistently and unequivocally n-uniform mosaics in a consistent manner with the current nomenclature used for the Archimedean (regular and semiregular) tessellations

Hydraulic engineering in the 21st century: Where to?

For centuries, hydraulic engineers were at the forefront of science. The last forty years marked a change of perception in our society with a focus on environmental sustainability and management, particularly in developed countries. Herein, the writer illustrates his strong belief that the future of hydraulic engineering lies upon a combination of innovative engineering, research excellence and higher education of quality. This drive continues a long tradition established by eminent scholars like Arthur Thomas IPPEN, John Fisher KENNEDY and Hunter ROUSE