Precipitation Type Specific Radar Reflectivity-Rain Rate Relationships for Warsaw, Poland

Penelitian ini bertujuan untuk mengetahui peningkatan penguasaan konsep dan kemampuan literasi sains siswa dengan menggunakan model pembelajaran kontekstual berbantuan multimedia. Metode dan desain penelitian yang digunakan adalah quasi experiment dengan pretest-posttest control group design. Subjek penelitiannya adalah kelas XI di kabupaten Subang, Jawa-Barat. Hasil penelitian menunjukkan Model Pembelajaran Kontekstual berbantuan multimedia secara signifikan mampu meningkatkan penguasaan konsep dan kemampuan literasi sains siswa. Peningkatan penguasaan konsep siswa dengan nilai N-Gain 0.50 (kategori sedang) untuk kelas eksperimen dan 0,30 (kategori sedang) untuk kelas kontrol. Peningkatan kemampuan literasi sains siswa dengan nilai N-Gain 0.45 (kategori sedang) untuk kelas eksperimen dan 0,30 (kategori sedang) untuk kelas kontrol. This study aims to determine the concepts mastery and skills increase scientific literacy of students by using multimedia-assisted contextual learning model. The method used quasi experiment with pretest-posttest control group design. Subjects of study are class XI in Subang districts, West-Java. The result of study showed that contextual model’s aided by multimedia significantly enhance student’s concepts mastery and skills scientific literacy. The enhancement of student’s concepts mastery with N-Gain value is 0.50 (medium category) for experiment class and 0,30 (medium category) for control class. The enhancement of student's skills scientific literacy with N-Gain value is 0.45 (medium category) for experiment class and 0,30 (medium category) for control class

The Use of Cluster Analysis to Evaluate the Impact of COVID-19 Pandemic on Daily Water Demand Patterns

Proper determination of unitary water demand and diurnal distribution of water consumption (water consumption histogram) provides the basis for designing, dimensioning, and all analyses of water supply networks. It is important in the case of mathematical modelling of flows in the water supply network, particularly during the determination of nodal water demands in the context of Extended Period Simulation (EPS). Considering the above, the analysis of hourly water consumption in selected apartment buildings was performed to verify the justification of the application of grouping by means of k-means clustering. The article presents a detailed description of the adopted methodology, as well as the obtained results in the form of synthetic distributions of hourly water consumption, and the effect of the COVID-19 pandemic on their change

Criteria for identifying maximum rainfall determined by the peaks-over-threshold (POT) method under the Polish Atlas of Rainfall Intensities (PANDa) project

Determination of rainfall maxima from long-term series is one of the more important tasks in urban hydrology. These maxima are useful both in designing land drainage systems and for flood protection in a catchment. The identification of rainfall maxima for the hierarchy of rainfall durations from 5 min to 4 320 min is a fundamental stage of the creation of the first version of the Polish Atlas of Rainfall Intensities (PANDa), which will ultimately be a source of updated and reliable information on design rainfall intensities for designing and modeling rainwater drainage and retention systems in Poland. One of the methods for identifying extreme rainfall events is to use criteria for selecting rainfall based on their depth for a given rainfall frequency and duration. Existing national experience in this respect is based on the results of analyses usually conducted with regard to records from single weather stations. This article presents the results of a study designed to verify the usefulness of the literature-based criteria for identifying rainfall maxima using the peaks-over-threshold (POT) method at a much broader nationwide scale. The study analyzed data from a previously created digital database of rainfall series, which includes 3 000 stationyears (consisting of a 30-year measurement series from 100 weather stations of the Institute of Meteorology and the Water Management - National Research Institute (IMGW-PIB). The study results show that as far as the investigated measurement series are concerned, the criteria based on the literature sources have limited application and can only be used for identifying the largest short-duration rainfall events. To determine rainfall maxima for all of the time intervals analyzed (from 5 minutes to 3 days), it was necessary to develop our own criteria that would allow the methodology for identifying extreme rainfall events to be standardized for all 100 stations

Modern proposal of methodology for retrieval of characteristic synthetic rainfall hyetographs

Modern engineering workshop of designing and modelling complex drainage systems is based on hydrodynamic modelling and has a probabilistic character. Its practical application requires a change regarding rainfall models accepted at the input. Previously used artificial rainfall models of simplified form, e.g. block precipitation or Euler's type II model rainfall are no longer sufficient. It is noticeable that urgent clarification is needed as regards the methodology of standardized rainfall hyetographs that would take into consideration the specifics of local storm rainfall temporal dynamics. The aim of the paper is to present a proposal for innovative methodology for determining standardized rainfall hyetographs, based on statistical processing of the collection of actual local precipitation characteristics. Proposed methodology is based on the classification of standardized rainfall hyetographs with the use of cluster analysis. Its application is presented on the example of selected rain gauges localized in Poland. Synthetic rainfall hyetographs achieved as a final result may be used for hydrodynamic modelling of sewerage systems, including probabilistic detection of necessary capacity of retention reservoirs

Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

An accurate air-temperature prediction can provide the energy consumption and system load in advance, both of which are crucial in HVAC (heating, ventilation, air conditioning) system operation optimisation as a way of reducing energy losses, operating costs, as well as pollution and dust emissions while maintaining residents’ thermal comfort. This article presents the results of an outdoor air-temperature time-series prediction for a multifamily building with the use of artificial neural networks during the heating period (October–May). The aim of the research was to analyse in detail the created neural models with a view to select the best combination of predictors and the optimal number of neurons in a hidden layer. To meet that task, the Akaike information criterion was used. The most accurate results were obtained by MLP 3-3-1 (r = 0.986, AIC = 1300.098, SSE = 4467.109), with the ambient-air-temperature time series observed 1, 2, and 24 h before the prognostic temperature as predictors. The AIC proved to be a useful method for the optimum model selection in a machine-learning modelling. What is more, neural network models provide the most accurate prediction, when compared with LR and SVR. Additionally, the obtained temperature predictions were used in HVAC applications: entering-water temperature and indoor temperature modelling

The separation of maximum amounts of precipitation for the Polish Atlas of Rains Intensities (PANDa)

In this paper selection of maximal precipitation amounts for Polish Atlas of Rains Intensities (PANDa) has been presented. PANDa supposes to be the source of actual and indisputable information about reliable rainfall intensities for designing of storm water drainage and retention systems in Poland. During the realization of the maximum amounts separation procedure, for chosen 100 meteorological stations with the use of peak over threshold (POT) method, a number of problems has been found, for which proceeding algorithms have been described

The impact of the number of high temporal resolution water meters on the determinism of water consumption in a district metered area

Abstract Developments in data mining techniques have significantly influenced the progress of Intelligent Water Systems (IWSs). Learning about the hydraulic conditions enables the development of increasingly reliable predictive models of water consumption. The non-stationary, non-linear, and inherent stochasticity of water consumption data at the level of a single water meter means that the characteristics of its determinism remain impossible to observe and their burden of randomness creates interpretive difficulties. A deterministic model of water consumption was developed based on data from high temporal resolution water meters. Seven machine learning algorithms were used and compared to build predictive models. In addition, an attempt was made to estimate how many water meters data are needed for the model to bear the hallmarks of determinism. The most accurate model was obtained using Support Vector Regression (8.9%) and the determinism of the model was achieved using time series from eleven water meters of multi-family buildings