Annals of Warsaw University of Life Sciences, Land Reclamation最新文献

{"title":"Weekly urban water demand forecasting using a hybrid wavelet–bootstrap–artificial neural network approach","authors":"K. Adamowski, J. Adamowski, O. Seidou, B. Ozga-Zieliński","doi":"10.2478/sggw-2014-0016","DOIUrl":"https://doi.org/10.2478/sggw-2014-0016","url":null,"abstract":"Abstract Weekly urban water demand forecasting using a hybrid wavelet-bootstrap-artificial neural network approach. This study developed a hybrid wavelet-bootstrap-artificial neural network (WBANN) model for weekly (one week) urban water demand forecasting in situations with limited data availability. The proposed WBANN method is aimed at improving the accuracy and reliability of water demand forecasting. Daily maximum temperature, total precipitation and water demand data for almost three years were used in this study. It was concluded that the hybrid WBANN model was more accurate compared to the ANN, BANN and WANN methods, and can be applied successfully for operational water demand forecasting. The WBANN model simulated peak water demand very effectively. The better performance of the WBANN model indicated that wavelet analysis significantly improved the model’s performance, whereas the bootstrap technique improved the reliability of forecasts by producing ensemble forecasts. The WBANN model was also found to be effective in assessing the uncertainty associated with water demand forecasts in terms of confidence bands; this can be helpful in operational water demand forecasting. Streszczenie Tygodniowa prognoza zapotrzebowania na wodę w obszarach miejskich określana metodą hybrydową z wykorzystaniem transformaty falkowej - bootstrapu - sztucznej sieci neuronowej. W artykule zaproponowano hybrydowy model (WBANN) wykorzystujący transformatę falkową, bootstrap i sztuczną sieć neuronową do opracowania tygodniowej prognozy zapotrzebowania na wodę w obszarach miejskich przy ograniczonej dostępności danych. Proponowany model WBANN ma na celu poprawę trafności i niezawodności prognozowania zaopatrzenia w wodę. W analizach wykorzystane zostały dobowe wartości maksymalnej temperatury, sumy opadów i zapotrzebowania na wodę z 3-letniego okresu obserwacji. Stwierdzono, że hybrydowy model WBANN jest dokładniejszy od modeli ANN, BANN i WANN i z powodzeniem może być użyty do operacyjnego prognozowania zapotrzebowania na wodę. Model WBANN bardzo skutecznie prognozuje szczytowy popyt na wodę. Dobre wyniki otrzymane z modelu WBANN świadczą o tym, że zastosowana analiza falkowa znacząco poprawiła dokładność modelu, a metoda bootstrapu polepszyła niezawodność (wiarygodność) modelu poprzez prognozowanie ensemblowe. Ocena niepewności z zastosowaniem przedziału ufności wykazała dużą trafność prognoz generowanych przez model WBANN oraz jego przydatność w operacyjnym wykorzystaniu","PeriodicalId":169511,"journal":{"name":"Annals of Warsaw University of Life Sciences, Land Reclamation","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114889541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Particulate matter in indoor spaces: known facts and the knowledge gaps*","authors":"P. Rogula-Kopiec, J. Pastuszka, W. Rogula-Kozłowska, G. Majewski","doi":"10.1515/sggw-2015-0013","DOIUrl":"https://doi.org/10.1515/sggw-2015-0013","url":null,"abstract":"Abstract: Particulate matter in indoor spaces: known facts and the knowledge gaps. As people spend most of the time in closed spaces (flats, workplaces, schools etc.), the indoor air has been researched for many years all over the world. Particulate matter (PM) is one of the most often examined pollutants in the indoor and outdoor air. The following study presents the facts about PM in closed spaces and the most often taken actions. The least known aspects related to the indoor air pollution with PM are demonstrated. The indoor space of various service and office buildings/facilities (not related to production, i.e. offices, shops, beauty parlours, restaurant kitchens, restaurants, pubs etc.) seem to be an unrecognized area in the air pollution studies. Importantly, a great number of people work in such spaces all over the world and thus spend there a large part of their lives.","PeriodicalId":169511,"journal":{"name":"Annals of Warsaw University of Life Sciences, Land Reclamation","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128931157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}