This paper assesses the utility of statistical models by examining methodological issues associated with empirical analysis of hydraulic and channel geometry relationships. It also describes and presents examples of advanced statistical models
Anuarul statistic al României, 1992 = Romanian statistical yearbook, 1992
Documentation ; Economy ; Population ; Romania ; Statistics ; Yearbook
Cette édition de 1992 comprend des chapitres et des indicateurs nouveaux, qui n'ont jamais été publiés au cours des 45 ans du régime totalitaire : environnement, structure ethnique de la population, consommation et dépenses de la population
Estimates of long-term immigration to the United States : moving US statistics toward United Nations concepts
US immigration data are revised to reflect the UN demographic concept of long-term immigration. The estimates of long-term immigration for 1983 are compared to official INS statistics on alien immigration. Significant differences emerge according
Annual variation ; Climatic anomaly ; Data processing ; Statistics ; Sweden ; Temperature ; Time series
The long record (1722-1989) of annual mean temperatures in Uppsala, is used to study how different features of temporal variations in the data are revealed by various simple statistical filters. Discussion of the results.
Applied climatology ; Arizona ; Environment ; Regression analysis ; Statistics ; Temperature ; United States ; Urban climate
An important hypothesis, and the major emphasis of this paper, is that the immediate surface temperature (Ts) at a site is directly correlated with the overlying air temperature (Ta), thus proving to be statistically significant in predicting Ta
Belgium ; Fog ; Forecast;Prediction ; Meteorology ; Meuse ; Model ; Statistics ; Valley
This paper presents a statistical model used to forecast fog in the Meuse valley in Belgium. The method is a bootstrap discriminant analysis using eight predictors : river surface temperature, air pressure, air temperature at two elevations, wind
distribution of suspended sediments. The statistical parameters show the change along the river in a non-linear fashion which may be due to human interference and due to different types of sediments contributed by tributaries to the Krishna river.
Flood ; Floodplain ; Fluvial processes ; Grain size distribution;Granulometry ; Model ; Numerical model ; Sedimentology ; Statistics ; United Kingdom ; Wales
to the Severn flood are used as input for a computer program of James' (1985) model. The pattern of sediment concentrations predicted by the model was compared with that obtained from statistical analysis of the flood sediment.