Conditional Acceptability for Random Variables

Acceptable random variables introduced by Giuliano Antonini et al. (J. Math. Anal. Appl. 338:1188-1203, 2008) form a class of dependent random variables that contains negatively dependent random variables as a particular case. The concept of acceptability has been studied by authors under various versions of the definition, such as extended acceptability or wide acceptability. In this paper, we combine the concept of acceptability with the concept of conditioning, which has been the subject of current research activity. For conditionally acceptable random variables, we provide a number of probability inequalities that can be used to obtain asymptotic results

Selection of the optimal unit of analysis in assessing the structure of terrestrial arthropods assemblages

Abstract The article shows the use of statistical methods of multivariate analysis on the example of environmental data – the species composition of terrestrial arthropods. It is shown that the research results largely depend on the scale of observations made – whether it is a study conducted in a whole ecosystem or in a separate landscape element, type of habitat or, finally, an analysis of elementary samples. The results and their interpretations directly depend on the scale chosen by the researcher. We were mainly concerned with the reliability of using of multidimensional statistics in practical situations.</jats:p

Zastosowanie uczenia maszynowego do analizy sygnału e-nosa we wczesnym wykrywaniu porażenia budynków

Mould that develops on moistened building barriers is a major cause of the Sick Building Syndrome (SBS). Fungi emit Volatile Organic Compounds (VOC) that can be detected in the indoor air using several techniques of detection e.g. chromatography but also using gas sensors arrays. All array sensors generate particular electric signals that ought to be analysed using properly selected statistical methods of interpretation. This work is focused on the attempt to apply unsupervised and supervised statistical classifying models in the evaluation of signals from gas sensors matrix to analyse the air sampled from the headspace of various types of the building materials at the different level of contamination but also clean reference materials.Grzyb rozwijający się na ścianach budynków jest głównym powodem zjawiska, które nazwano Syndromem Chorego Budynku. Wolne związki organiczne emitowane przez grzyby mogą być wykryte różnymi metodami, m.in. na podstawie chromatografii, ale także za pomocą matryc czujników gazowych. Wszystkie tego typu narzędzia generują sygnały elektryczne, które można analizować za pomocą odpowiednich technik statystycznych. Praca skupia się na zastosowaniu nadzorowanych i nienadzorowanych technik uczenia maszynowego w ocenie sygnału pochodzącego z elektronicznego nosa

Use of multidimensional testing to evaluate the impacts of treated wastewater discharge on river water quality - Hotelling test case

Abstract Water bodies often suffer from the discharge of nutrient loading from agricultural and urban areas that compromises the quality of water. This study presents the application of the Hotelling test to evaluate the impacts of treated wastewater, discharged from a municipal wastewater treatment plant (WWTP), on the quality of river water. The quality of water was described by different pollution indicators, including COD, BOD5, TSS, NH4-N, NO2-N, NO3-N, TKN, TN and TP. The water samples were collected at three different locations: 500 m above the discharge point, at the wastewater discharge point and 1000 m below the wastewater discharge point. The tests of single pollution indicator showed differences between the two locations. Specifically, the results show that each single comparison controlled type I error at 0.05, while the family-wise error rate for the tests of all marginal hypotheses was controlled at 0.37. Testing for single indicators separately may not reveal true multivariate differences. In order to overcome this limitation, a modified version of T2 Hotelling test was used with robust James-Stein type estimators of covariance matrix. Major differences in the overall water quality were observed mainly for the concentration of nitrogenous compounds and found to significantly influence the water quality of the receiving river.</jats:p

Analysis of Sewer Network Performance in the Context of Modernization: Modeling, Sensitivity, and Uncertainty Analysis

Specific flood volume and degree of flooding are important parameters for evaluating the performance of stormwater networks. Hydrodynamic models are usually used to calculate these important measures, but this task requires the collection of detailed data on land use, the sewer network, rainfall, and flows, which are not always possible to obtain. The present research consists in the development of a methodology, using the USEPA Stormwater Management Model (SWMM), for simulating the performance of a stormwater network to determine whether it is in need of modernization. This determination is based on independent variables including rainfall data, catchment retention, and channel capacity. A logistic regression was developed to assess the sewer network performance based on simulation of specific flood volume and degree of flooding in the context of modernization. An extended sensitivity analysis was also used to assess the influence of rainfall intensity on the results of sensitivity coefficient calculations for the calibrated SWMM parameters. Using the extreme gradient boosting method, a tool has been developed to optimize the combination of SWMM parameters, reducing the uncertainty of simulation results, which can be used in the selection of their measurement methods prior to model development. It has been shown that, using the logistic regression model, it is possible to rapidly simulate the operation of a stormwater system to assess its need for modernization. It was confirmed that an increase in rainfall intensity leads to a significant decrease in the values of the calculated sensitivity coefficients associated with the SWMM parameters. The highest sensitivity coefficient was shown for a correction coefficient for percentage of impervious areas; for rainfall intensity 33-133 L.(s.ha)(-1) varied from 1.45 to 12.38. This result leads to a method for selecting specific rainfall events for calibration of the model, thereby improving the ability to assess the performance of the stormwater system. Interestingly, however, for the exemplary catchment in Kielce, Poland, the generalized likelihood uncertainty estimation (GLUE) method was used, combined with the XGboost machine learning technique, to determine that the reliability of the SWMM parameters has a negligible impact on the probability of a stormwater network failure. (C) 2022 American Society of Civil Engineers

Comparison of the use of species abundance and presence-absence data for diversity assessment

Abstract The article is devoted to the analysis of empirical data on the distribution of ground beetles in three model sites located in Lublin (Poland). Using Principal Coordinates Analysis (PCoA) and hierarchical cluster analysis, we compared the results of the studies based on the data of species × abundance and binary data (species × presence / absence). It was shown that the hierarchical clustering method and PCoA based on binary data demonstrate the individuality of the studied territories, although they have some common species. While the results of the analysis, based on abundances, did not show a clear separation of the stations within the three studied locations, the similarity between the studied territories is more objectively reflected from a biological point of view.</jats:p

Advanced sensitivity analysis of the impact of the temporal distribution and intensity of rainfall on hydrograph parameters in urban catchments

Knowledge of the variability of the hydrograph of outflow from urban catchments is highly important for measurements and evaluation of the operation of sewer networks. Currently, hydrodynamic models are most frequently used for hydrograph modeling. Since a large number of their parameters have to be identified, there may be problems at the calibration stage. Hence, sensitivity analysis is used to limit the number of parameters. However, the current sensitivity analytical methods ignore the effect of the temporal distribution and intensity of precipitation in a rainfall event on the catchment outflow hydrograph. This article presents a methodology of constructing a simulator of catchment outflow hydrograph parameters (volume and maximum flow). For this purpose, uncertainty analytical results obtained with the use of the GLUE (generalized likelihood uncertainty estimation) method were used. A novel analysis of the sensitivity of the hydrodynamic catchment models was also developed, which can be used in the analysis of the operation of stormwater networks and underground infrastructure facilities. Using the logistic regression method, an innovative sensitivity coefficient was proposed to study the impact of the variability of the parameters of the hydrodynamic model depending on the distribution of rainfall, the origin of rainfall (on the Chomicz scale), and the uncertainty of the estimated simulator coefficients on the parameters of the outflow hydrograph. The developed model enables the analysis of the impact of the identified SWMM (Storm Water Management Model) parameters on the runoff hydrograph, taking into account local rainfall conditions, which have not been analyzed thus far. Compared with the currently developed methods, the analyses included the impact of the uncertainty of the identified coefficients in the logistic regression model on the results of the sensitivity coefficient calculation. This aspect has not been taken into account in the sensitivity analytical methods thus far, although this approach evaluates the reliability of the simulation results. The results indicated a considerable influence of rainfall distribution and intensity on the sensitivity factors. The greater the intensity and rainfall were, the lower the impact of the identified hydrodynamic model parameters on the hydrograph parameters. Additionally, the calculations confirmed the significant impact of the uncertainty of the estimated coefficient in the simulator on the sensitivity coefficients. In the context of the sensitivity analysis, the obtained results have a significant effect on the interpretation of the relationships obtained. The approach presented in this study can be widely applied at the model calibration stage and for appropriate selection of hydrographs for identification and validation of model parameters. The results of the calculations obtained in this study indicate the suitability of including the origin of rainfall in the sensitivity analysis and calibration of hydrodynamic models, which results from the different sensitivities of models for normal, heavy, and torrential rain types. In this context, it is necessary to first divide the rainfall data by origin, for which analyses will be performed, including sensitivity analysis and calibration.Considering the obtained results of the calculations, at the stage of identifying the parameters of hydrodynamic models and their validation, precipitation conditions should be included because, for the precipitation caused by heavy rainfall, the values of the sensitivity coefficients were much lower than for torrential ones. Taking into account the values of the sensitivity coefficients obtained, the calibration of the models should not only cover episodes with high rainfall intensity, since this may lead to calculation errors at the stage of applying the model in practice (assessment of the stormwater system operating conditions, design of reservoirs and flow control devices, green infrastructure, etc.)