82 research outputs found
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. J Industr Ecol 11:1–6Brown RR, Keath N, Wong THF (2009) Urban water management in cities: Historical, current and future regimes. Water Sci Technol 59:847–855De Graaf R, van de Giessen N, van de Ven F (2007a) Alternative water management options to reduce vulnerability for climate change in the Netherlands. Nat Hazards 5:407–422De Graaf RE, van de Giessen NC, van de Ven FHM (2007b) The closed city as a strategy to reduce vulnerability of urban areas for climate change. Water Sci Technol 56:165–173EEA (2010) European Environment Agency. The European environment. State and outlook 2010. Synthesis. Copenhagen, DenmarkEEA (2012) European Environment Agency. Urban adaptation to climate change in Europe. Challenges and opportunities for cities together with supportive national and European policies. Synthesis, Copenhagen, DenmarkEFILWC (2006) First European quality of life survey: participation in civil society. European Foundation for the Improvement of Living and Working Conditions, Dublin. http://www.eurofound.europa.eu/publications/htmlfiles/ef0676.htm . Accessed 21 February 2011Engel K, Jokiel D, Kraljevic A, Geiger M, Smith K (2011) Big cities. Big water. Big challenges. Water in an urbanizing world. World wildlife fund, KoberichEnvironmental Performance Index (2010) http://www.epi2010.yale.edu/Metrics/WaterEffectsOnEcosystem . Accessed 30 March 2012European Commission (2012) Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions. A Blueprint to Safeguard Europe’s Water Resources. COM (2012)673 finalEuropean Commission (2013) European Innovation Partnership on water (EIP Water). http://ec.europa.eu/environment/water/innovationpartnership/European green city index (2009) Assessing the environmental impact of Europe’s major cities. A research project conducted by the Economist Intelligence Unit, http://www.siemens.com/press/pool/de/events/corporate/2009-12-Cop15/European_Green_City_Index.pdf . Accessed 20 February 2011Grimm NB, Faeth SH, Golubiewski NE, Redman CL, Wu J, Bai X, Briggs JM (2008) Global change and the ecology of cities. Science 319(5864):756–760Hoekstra AY, Mekonnen MM, Chapagain AK, Mathews RE, Richter BD (2012) Global monthly water scarcity: Blue water footprints versus blue water availability. PLoS ONE 7(2):e32688. doi: 10.1371/journal.pone.0032688IMF (2012) Gross Domestic Product (international dollars) as provided by the International Monetary Fund for 2010–2011: http://en.wikipedia.org/wiki/List_of_countries_by_GDP_(PPP)_per_capita . Accessed October 2012Kaufman D, Kraay A, Mastruzzi M (2010) The worldwide governance indicators. Methodology and analytical issues. World Bank Policy Research Working Paper 5430. World Bank, Washington DCLange P, Driessen PJ, Sauer A, Borneman B, Burger P (2013) Governing towards sustainability – conceptualizing modes of governance. J Environ Policy Planning 15:403–425Makropoulos CK, Butler D (2010) Distributed water infrastructure for sustainable communities. Water Resour Manag 24(11):2795–2816Mekonnen MM, Hoekstra AY (2011) National water footprint accounts: the green, blue and grey water footprint of production and consumption. Volumes 1 and 2. Value of Water Research Report Series No. 50. UNESCO-IHE, Delft, the NetherlandsNorman E, Bakker K, Cook C, Dunn G, Allen D (2010) Water security: A primer. Policy report. Fostering water security in Canada Project www.watergovernance.ca Accessed 20 September 2013Philip R, Anton B, van der Steen P (2011) SWITCH training kit. Integrated urban water management in the city of the future. Module 1. Strategic planning, ICLEI, Freiburg, GermanyPrüss-Üstün A, Bos R, Gore F, Bartram J (2008) Safer water, better health: Costs, benefits and sustainability of interventions to protect and promote health. World Health Organization, GenevaRozos E, Makropoulos C (2013) Source to tap urban water cycle modelling. Environ Model Softw 41:139–150SIWI (2012) Stockholm International Water Institute. Statistics. http://www.siwi.org/sa/node.asp?node=159 Accessed 20 December, 2012Ugarelli R, Pachioli M, Di Federico V (2009) Planning maintenance strategies for Italian urban drainage systems applying CARE-S. In: Allegre H, do Céu Almeida M (eds) Strategic asset management of water supply and wastewater infrastructures. IWA Publishing, London, pp 471–486UN (2012) World urbanization prospects: The 2011 revision. UN, New York, USA. http://esa.un.org/unup/ . Accessed 30 November 2012UNDP (2004) Water governance for poverty reduction. USA, New YorkUNEP (2008) Every drop counts; environmentally sound technologies for urban and domestic water use efficiency. Switzerland, GenevaUNEP (2012) Fifth global environment outlook: Environment for the future we want. Switzerland, GenevaUNESCO (2012) Managing water under uncertainty and risk. Facts and figures from the UN world water development report 4. http://unesdoc.unesco.org/images/0021/002154/215492e.pdf . Accessed 20 December 2012UN-Habitat (2010). Climate change strategy 2010–2013. Urban Environmental Planning Branch, Nairobi, Kenia. http://www.google.nl/search?sourceid=navclient&ie=UTF-8&rlz=1T4MXGB_enNL512NL512&q=Climate+change+strategy+2010-2013 . Accessed 20 December 2012Van Leeuwen CJ (2007) Introduction. In: Van Leeuwen CJ, Vermeire TG (eds) Risk Assessment of Chemicals. An Introduction. Springer, Dordrecht, 2nd edn, pp. 1–36Van Leeuwen CJ, Frijns J, Van Wezel A, Van De Ven FHM (2012) City blueprints: 24 indicators to assess the sustainability of the urban water cycle. Wat Resour Manage 26:2177–2197Van Leeuwen CJ, Chandy PC (2013) The city blueprint: Experiences with the implementation of 24 indicators to assess the sustainability of the urban water cycle. Water Sci Technol 13(3):769–781Van Leeuwen K, Marques RC (2013) Current state of sustainability of urban water cycle services. Transition to the Urban Water Services of tomorrow (TRUST) report D11.1. http://www.trust-i.net/downloads/index.php?iddesc=682030 Water Resources Group (2009) Charting our water future. Economic framework to inform decisionmaking. West Perth, USA. http://www.mckinsey.com/App_Media/Reports/Water/Charting_Our_Water_Future_Full_Report_001.pdf . Accessed 20 February 2011World Bank (2013) Worldwide Governance Indicators. http://info.worldbank.org/governance/wgi/index.asp . Accessed 30 March 2013.World Economic Forum (2013) Global Risks, 8th edn. Geneva, Switzerland. http://reports.weforum.org/global-risks-2013/ Accessed 30 March 201
Methodology for assessment of the accessibility of a brown coal deposit with Analytical Hierarchy Process and Weighted Linear Combination
- …