The article presents a method of designing single-chamber rectangular detention reservoirs based on nomographs connecting the parameters and the shape of the inflow with the reservoir hydrograph (triangular, described by the power function and described by the gamma distribution) as well as the hydraulic characteristics of the accumulation chamber and the orifice. The preparation of nomographs involved using the SWMM (Storm Water Management Model) program with the application of numerical calculations’ results of a differential equation for the stormwater volume balance. The performed analyses confirm a high level of similarity between the results of calculating the reservoir volume obtained by using the above mentioned program and using the developed nomographs. The examples of calculations presented in the paper confirm the application aspects of the discussed method of designing the detention reservoir. Moreover, based on the conducted analyses it was concluded that the inflow hydrograph described by the gamma distribution has the greatest impact on the reservoir’s storage volume, whereas the hydrograph whose shape in the rise and recession phases is described by the power function has the smallest effect.
The paper formulates some objections to the methods of evaluation of uncertainty in noise measurement which are presented in two standards: ISO 9612 (2009) and DIN 45641 (1990). In particular, it focuses on approximation of an equivalent sound level by a function which depends on the arithmetic average of sound levels. Depending on the nature of a random sample the exact value of the equivalent sound level may be significantly different from an approximate one, which might lead to erroneous estimation of the uncertainty of noise indicators. The article presents an analysis of this problem and the adequacy of the solution depending on the type of a random sample.
The aim of the study was to evaluate the possibility of applying different methods of data mining to model the inflow of sewage into the municipal sewage treatment plant. Prediction models were elaborated using methods of support vector machines (SVM), random forests (RF), k-nearest neighbour (k-NN) and of Kernel regression (K). Data consisted of the time series of daily rainfalls, water level measurements in the clarified sewage recipient and the wastewater inflow into the Rzeszow city plant. Results indicate that the best models with one input delayed by 1 day were obtained using the k-NN method while the worst with the K method. For the models with two input variables and one explanatory one the smallest errors were obtained if model inputs were sewage inflow and rainfall data delayed by 1 day and the best fit is provided using RF method while the worst with the K method. In the case of models with three inputs and two explanatory variables, the best results were reported for the SVM and the worst for the K method. In the most of the modelling runs the smallest prediction errors are obtained using the SVM method and the biggest ones with the K method. In the case of the simplest model with one input delayed by 1 day the best results are provided using k-NN method and by the models with two inputs in two modelling runs the RF method appeared as the best.