An integrated system dynamics - Cellular automata model for distributed water-infrastructure planning

PublishedJournal ArticleThis is the author accepted manuscript. The final version is available from IWA Publishing via the DOI in this record.© IWA Publishing 2016.Modern distributed water-Aware technologies (including, for example, greywater recycling and rainwater harvesting) enable water reuse at the scale of household or neighbourhood. Nevertheless, even though these technologies are, in some cases, economically advantageous, they have a significant handicap compared to the centralized urban water management options: It is not easy to estimate a priori the extent and the rate of the technology spread. This disadvantage is amplified in the case of additional uncertainty due to expansion of an urban area. This overall incertitude is one of the basic reasons the stakeholders involved in urban water are sceptical about the distributed technologies, even in the cases where these appear to have lower cost. In this study, we suggest a methodology that attempts to cope with this uncertainty by coupling a cellular automata (CA) and a system dynamics (SD) model. The CA model is used to create scenarios of urban expansion including the suitability of installing water-Aware technologies for each new urban area. Then, the SD model is used to estimate the adoption rate of the technologies. Various scenarios based on different economic conditions and water prices are assessed. The suggested methodology is applied to an urban area in Attica, Greece.This research has been co-financed by the European Union (European Social Fund– ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES. Investing in knowledge society through the European Social Fund. Hydropolis: Urban development and water infrastructure - Towards innovative decentralized urban water management

KNN vs. Bluecat—Machine Learning vs. Classical Statistics

Uncertainty is inherent in the modelling of any physical processes. Regarding hydrological modelling, the uncertainty has multiple sources including the measurement errors of the stresses (the model inputs), the measurement errors of the hydrological process of interest (the observations against which the model is calibrated), the model limitations, etc. The typical techniques to assess this uncertainty (e.g., Monte Carlo simulation) are computationally expensive and require specific preparations for each individual application (e.g., selection of appropriate probability distribution). Recently, data-driven methods have been suggested that attempt to estimate the uncertainty of a model simulation based exclusively on the available data. In this study, two data-driven methods were employed, one based on machine learning techniques, and one based on statistical approaches. These methods were tested in two real-world case studies to obtain conclusions regarding their reliability. Furthermore, the flexibility of the machine learning method allowed assessing more complex sampling schemes for the data-driven estimation of the uncertainty. The anatomisation of the algorithmic background of the two methods revealed similarities between them, with the background of the statistical method being more theoretically robust. Nevertheless, the results from the case studies indicated that both methods perform equivalently well. For this reason, data-driven methods can become a valuable tool for practitioners

Urban water system metabolism assessment using WaterMet2 model

12th International Conference on Computing and Control for the Water Industry, CCWI2013, 2013-09-06, 2013-09-09, Perugia, ItalyThis paper presents a new "WaterMet2" model for integrated modelling of an urban water system (UWS). The model is able to quantify the principal water flows and other main fluxes in the UWS. The UWS in WaterMet2 is characterised using four different spatial scales (indoor area, local area, subcatchment and system area) and a daily temporal resolution. The main subsystems in WaterMet2 include water supply, water demand, wastewater and cyclic water recovery. The WaterMet2 is demonstrated here through modelling of the urban water system of Oslo city in Norway. Given a fast population growth, WaterMet2 analyses a range of alternative intervention strategies including 'business as usual', addition of new water resources, increased rehabilitation rates and water demand schemes to improve the performance of the Oslo UWS. The resulting five intervention strategies were compared with respect to some major UWS performance profiles quantified by the WaterMet2 model and expert's opinions. The results demonstrate how an integrated modelling approach can assist planners in defining a better intervention strategy in the future.This work was carried out as part of the ‘TRansition to Urban water Services of Tomorrow’ (TRUST) project. The authors wish to acknowledge the European Commission for funding TRUST project in the 7th Framework Programme under Grant Agreement No. 265122

City Blueprints: Baseline Assessments of Sustainable Water Management in 11 Cities of the Future

The necessity of Urban Water Cycle Services (UWCS) adapting to future stresses calls for changes that take sustainability into account. Megatrends (e.g. population growth, water scarcity, pollution and climate change) pose urgent water challenges in cities. In a previous paper, a set of indicators, i.e., the City Blueprint has been developed to assess the sustainability ofUWCS (Van Leeuwen et al.,Wat Resour Manage 26:2177¿2197, 2012). In this paper this approach has been applied in 9 cities and regions in Europe (Amsterdam, Algarve, Athens, Bucharest, Hamburg, Reggio Emilia, Rotterdam, Oslo and Cities of Scotland) and in 2 African cities in Angola (Kilamba Kiaxi) and Tanzania (Dar es Salaam). The assessments showed that cities vary considerably with regard to the sustainability of theUWCS. This is also captured in the Blue City Index (BCI), the arithmetic mean of 24 indicators comprising the City Blueprint (Van Leeuwen et al., Wat Resour Manage 26:2177¿2197, 2012). Theoretically, the BCI has a minimum score of 0 and a maximum score of 10. The actual BCIs in the 11 cities studied varied from 3.31 (Kilamba Kiaxi) to 7.72 (Hamburg). The BCI was positively correlated with the Gross Domestic Product (GDP) per person, the ambitions of the local authorities regarding the sustainability of the UWCS, the voluntary participation index (VPI) and all governance indicators according to the World Bank. The study demonstrated that the variability in sustainability among the UWCS of cities offers great opportunities for short-term and long-term improvements, provided that cities share their best practices.Van Leeuwen, CJ. (2013). City Blueprints: Baseline Assessments of Sustainable Water Management in 11 Cities of the Future. Water resources management. https://doi.org/10.1007/s11269-013-0462-5Bai X (2007) Industrial ecology and the global impacts of cities. 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