The paper aimed to
provide a review of different tools that estimate how human behavior changes by
water management strategies and quantify this change to support the decisions
of urban water managers. To support decision makers,
it is essential to be able to model the urban water system’s human part
explicitly and link it to the hydro system’s response, rather than only explore
the reaction of the system based on scenarios. To do so, tools are needed that
can model the human part of the system, explore its reaction to potential
changes and dynamically link back this to the techno-environmental model of the
water system. This work reviews state-of-the-art ABMs that are publicly
available focusing on the human part of the urban water system in Europe. The
review leads to the proposals of three pillars for future development of ABMs
for urban water management in Europe: end-user enablement; Machine Learning and
Artificial Intelligence integration and adversaries modelling.
References
[1]
Pande, S. and Sivapalan, M. (2017) Progress in Socio-Hydrology: A Meta-Analysis of Challenges and Opportunities. Wiley Interdisciplinary Reviews: Water, 4, Article No. e1193. https://doi.org/10.1002/wat2.1193
[2]
Keath, N.A. and Brown, R.R. (2009) Extreme Events: Being Prepared for the Pitfalls with Progressing Sustainable Urban Water Management. Water Science and Technology, 59, 1271-1280. https://doi.org/10.2166/wst.2009.136
[3]
Koutiva, I., Gerakopoulou, P., Makropoulos, C. and Vernardakis, C. (2017) Exploration of Domestic Water Demand Attitudes Using Qualitative and Quantitative Social Research Methods. Urban Water Journal, 14, 307-314. https://doi.org/10.1080/1573062X.2015.1135968
[4]
Makropoulos, C. and Savic, D.A. (2019) Urban Hydroinformatics: Past, Present and Future. Water, 11, Article No. 1959. https://doi.org/10.3390/w11101959
[5]
Behzadian, K., Kapelan, Z., Venkatesh, G., Brattebø, H., Sægrov, S., Rozos, E., Makropoulos, C., Ugarelli, R., Milina, J. and Hem, L. (2014) Urban Water System Metabolism Assessment Using WaterMet2 Model. Procedia Engineering, 70, 113-122. https://doi.org/10.1016/j.proeng.2014.02.014
[6]
Kim, S., Jang, Ch., Kim, H.J., Park, J.H., Cho, H.S. and Kim, H.R. (2019) DWAT—User’s Manual v1.1. Han River Flood Control Office, Seoul.
[7]
DHI Group (n.d.) Urban Water. https://www.dhigroup.com/areas-of-expertise/urban-water
[8]
Rozos, E. and Makropoulos C. (2013) Source to Tap Urban Water Cycle Modeling. Environmental Modelling and Software, 41, 139-150. https://doi.org/10.1016/j.envsoft.2012.11.015
[9]
Baki, S., Rozos, E. and Makropoulos, C. (2018) Designing Water Demand Management Schemes Using a Socio-Technical Modelling Approach. Science of the Total Environment, 622-623, 1590-1602. https://doi.org/10.1016/j.scitotenv.2017.10.041
[10]
Guidolin, M., Chen, A.S., Ghimire, B., Keedwell, E.C., Djordjevic, S. and Savic, D.A. (2016) A Weighted Cellular Automata 2D Inundation Model for Rapid Flood Analysis. Environmental Modelling & Software, 84, 378-394. https://doi.org/10.1016/j.envsoft.2016.07.008
[11]
DHI Group (n.d.) Mike Flood. https://www.mikepoweredbydhi.com/products/mike-flood
[12]
Rossman, L.A. (2000) EPANET 2 USERS MANUAL. EPA/600/R-00/057, U.S. Environmental Protection Agency, Washington DC.
[13]
Karavokiros, G., Nikolopoulos, D., Manouri, S., Efstratiadis, A., Makropoulos, C., Mamassis, N. and Koutsoyiannis, D. (2020) Hydronomeas 2020: Open-Source Decision Support System for water Resources Management. EGU General Assembly Conference Abstracts, Vienna, May 2020, Article ID: 20022. https://doi.org/10.5194/egusphere-egu2020-20022
[14]
Urich, C. and Rauch, W. (2014) Exploring Critical Pathways for Urban Water Management to Identify Robust Strategies under Deep Uncertainties. Water Research, 66, 374-389. https://doi.org/10.1016/j.watres.2014.08.020
[15]
Pahl-Wostl, C. (2007) Adaptive Water Management: How to Cope with Uncertainty. Policy Brief No.4, Integrated Project of the 6th EU Framework Programme, NeWater Project Consortium, Osnabrueck, Germany.
[16]
Macy, M.W. and Willer, R. (2001) From Factors to Actors: Computational Sociology and Agent-Based Modeling. Cornell University, Ithaca.
[17]
Filatova, T., Verburg, P.H., Parker, D.C. and Stannard, C.A. (2013) Spatial Agent-Based Models for Socio-Ecological Systems: Challenges and Prospects. Environmental Modelling & Software, 45, 1-7. https://doi.org/10.1016/j.envsoft.2013.03.017
[18]
van Dam, K.H., Nikolic, I. and Lukszo, Z. (Eds.) (2013) Agent-Based Modelling of Socio-Technical Systems. Springer, Dordrecht.
[19]
Nilsson, F. and Darley, V. (2006) On Complex Adaptive Systems and Agent-Based Modelling for Improving Decision-Making in Manufacturing and Logistics Settings: Experiences from a Packaging Company. International Journal of Operations & Production Management, 26, 1351-1373. https://doi.org/10.1108/01443570610710588
[20]
Borshchev, A. and Filippov, A. (2004) From System Dynamics and Discrete Event to Practical Agent Based Modeling: Reasons, Techniques, Tools. Proceedings of the 22nd International Conference of the System Dynamics Society, Oxford, 25-29 July 2004.
[21]
Tesfatsion, L. (2003) Agent-Based Computational Economics: Modeling Economies as Complex Adaptive Systems. Information Sciences, 149, 262-268. https://doi.org/10.1016/S0020-0255(02)00280-3
[22]
Railsback, S.F. (2001) Concepts from Complex Adaptive Systems as a Framework for Individual-Based Modelling. Ecological Modelling, 139, 47-62. https://doi.org/10.1016/S0304-3800(01)00228-9
[23]
Wooldridge M. (1999) Intelligent Agents. In: Weiss G., Ed., Multiagent Systems, The MIT Press, Cambridge, 27-77.
[24]
Hammond, R.A. (2015) Appendix A: Considerations and Best Practices in Agent-Based Modeling to Inform Policy. In: Committee on the Assessment of Agent-Based Models to Inform Tobacco Product Regulation; Board on Population Health and Public Health Practice; Institute of Medicine; Wallace, R., Geller, A. and Ogawa, V.A., Eds., Assessing the Use of Agent-Based Models for Tobacco Regulation, National Academies Press (US), Washington DC, 161-193. https://www.ncbi.nlm.nih.gov/books/NBK305917/
[25]
An, L., Grimm, V. and Turner II, B.L. (2020) Editorial: Meeting Grand Challenges in Agent-Based Models. Journal of Artificial Societies and Social Simulation, 23, Article No. 13. https://www.jasss.org/23/1/13.html https://doi.org/10.18564/jasss.4012
[26]
Grimm, V., Berger, U., De Angelis, D.L., Polhill, J.G., Giske, J. and Railsback, S.F. (2010) The ODD Protocol: A Review and First Update. Ecological Modelling, 221, 2760-2768. https://doi.org/10.1016/j.ecolmodel.2010.08.019
[27]
OPEN ABM (n.d.) https://catalog.comses.net
[28]
NetLogo (1999) Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston. http://ccl.northwestern.edu/netlogo/
[29]
Samuelson, D.A. and Macal, C.M. (2006) Agent-Based Simulation Comes of Age. ORMS Today, 33, Article No. 34.
[30]
Anylogic (n.d.). http://www.anylogic.com
[31]
Alvi, M.S.Q., Mahmood, I., Javed, F., Malik, A.W. and Sarjoughian, H. (2019) Dynamic Behavioural Modeling, Simulation and Analysis of Household Water Consumption in an Urban Area: A Hybrid Approach. Proceedings of the Winter Simulation Conference, National Harbor Maryland, 8-12 December 2018, 2411-2422.
[32]
Koutiv, I. and Makropoulos, C. (2016) Modelling Domestic Water Demand: An Agent Based Approach. Environmental Modelling and Software, 79, 35-54. https://doi.org/10.1016/j.envsoft.2016.01.005
[33]
Zhuge, C., Yu, M., Wang, C., Cui, Y. and Liu, Y. (2020) An Agent-Based Spatiotemporal Integrated Approach to Simulating In-Home Water and Related Energy Use Behavior: A Test Case of Beijing, China. Science of the Total Environment, 708, Article ID: 135086.
[34]
Pahl-Wostl, C. and Hare, M. (2004) Processes of Social Learning in Integrated Resources Management. Journal of Community and Applied Social Psychology, 14, 193-206. https://doi.org/10.1002/casp.774
[35]
Athanasiadis, I.N., Mentes A.K., Mitkas, P.A. and Mylopoulos, Y.A. (2005) A Hybrid Agent-Based Model for Estimating Residential Water Demand. Simulation, 81, 175-187. https://doi.org/10.1177%2F0037549705053172
[36]
Mashhadi Ali, A., Shafiee, M.E. and Berglund, E.Z. (2017) Agent-Based Modeling to Simulate the Dynamics of Urban Water Supply: Climate, Population Growth, and Water Shortages. Sustainable Cities and Society, 28, 420-434. https://doi.org/10.1016/j.scs.2016.10.001
[37]
Moglia, M., Perez P. and Burn S. (2010) Modelling an Urban Water System on the Edge of Chaos. Environmental Modelling and Software, 25, 1528-1538. https://doi.org/10.1016/j.envsoft.2010.05.002
[38]
Bakhtiari, P.H., Nikoo, M.R., Izady, A. and Talebbeydokhti, N. (2020) A Coupled Agent-Based Risk-Based Optimization Model for Integrated Urban Water Management. Sustainable Cities and Society, 53, Article ID: 101922.
[39]
Zozmann, H., Klassert, C., Sigel, K., Gawel, E. and Klauer, B. (2019) Commercial Tanker Water Demand in Amman, Jordan—A Spatial Simulation Model of Water Consumption Decisions under Intermittent Network Supply. Water, 11, Article No. 254. https://doi.org/10.3390/w11020254
[40]
Ding, K.J., Gilligan, J.M., Yang, Y.C.E., Wolski, P. and Hornberger, G.M. (2021) Assessing Food-Energy-Water Resources Management Strategies at City Scale: An Agent-Based Modeling Approach for Cape Town, South Africa. Resources, Conservation and Recycling, 170, Article ID: 105573. https://doi.org/10.1016/j.resconrec.2021.105573
[41]
Kotz, C. and Hiessl, H. (2005) Analysis of System Innovation in Urban Water Infrastructure Systems: An Agent-Based Modelling Approach. Water Science and Technology: Water Supply, 5, 135-144. https://doi.org/10.2166/ws.2005.0030
[42]
Schwarz, N. and Ernst, A. (2009) Agent-Based Modeling of the Diffusion of Environmental Innovations—An Empirical Approach. Technological Forecasting and Social Change, 76, 497-511. https://doi.org/10.1016/j.techfore.2008.03.024
[43]
Giacomoni, M.H. and Berglund, E.Z. (2015) Complex adaptive Modeling Framework for Evaluating Adaptive Demand Management for Urban Water Resources Sustainability. Journal of Water Resources Planning and Management, 141, Article ID: 04015024. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000543
[44]
Ramsey, E., Pesantez, J., Fasaee, M.A.K., Dicarlo, M., Monroe, J. and Berglund, E.Z. (2020) A Smart Water Grid for Micro-Trading Rainwater: Hydraulic Feasibility Analysis. Water, 12, Article No. 3075. https://doi.org/10.3390/w12113075
[45]
Galán, J.M., López-Paredes, A. and Del Olmo, R. (2009) An Agent-Based Model for Domestic Water Management in Valladolid Metropolitan Area. Water Resources Research, 45, Article No. W05401. https://doi.org/10.1029/2007WR006536
[46]
Wang, Y., Zhou, Y., Franz, K., Zhang, X., Ding, K.J., Jia, G. and Yuan, X. (2021) An Agent-Based Framework for High-Resolution Modeling of Domestic Water Use. Resources, Conservation and Recycling, 169, Article ID: 105520. https://doi.org/10.1016/j.resconrec.2021.105520
[47]
Iglesias-Rey, P.L., Martínez-Solano, F.J. and Ribelles-Aquilar, J.V. (2017) Extending EPANET Capabilities with Add-in Tools. Procedia Engineering, 186, 626-634. https://doi.org/10.1016/j.proeng.2017.03.279
[48]
Chu, J., Wang, C., Chen, J. and Wang, H. (2009) Agent-Based Residential Water Use Behavior Simulation and Policy Implications: A Case-Study in Beijing City. Water Resources Management, 23, Article No. 3267. https://doi.org/10.1007/s11269-009-9433-2
[49]
Jenkins, K., Surminski, S., Hall, J. and Crick, F. (2017) Assessing Surface Water Flood Risk and Management Strategies under Future Climate Change: Insights from an Agent-Based Model. Science of the Total Environment, 595, 159-168. https://doi.org/10.1016/j.scitotenv.2017.03.242
[50]
Koutiva, I., Lykou, A., Pantazis, C. and Makropoulos, C. (2020) Investigating Decision Mechanisms of Statutory Stakeholders in Flood Risk Strategy Formation: A Computational Experiments Approach. Water, 12, Article No. 2716. https://doi.org/10.3390/w12102716
[51]
Löwe, R., Urich, C., Sto. Domingo, N., Mark, O., Deletic, A. and Arnbjerg-Nielsen, K. (2017) Assessment of Urban Pluvial Flood Risk and Efficiency of Adaptation Options through Simulations—A New Generation of Urban Planning Tools. Journal of Hydrology, 550, 355-367. https://doi.org/10.1016/j.jhydrol.2017.05.009
[52]
Abebe, Y.A., Ghorbani, A., Nikolic, I., Vojinovic, Z. and Sanchez, A. (2019) Flood Risk Management in Sint Maarten—A Coupled Agent-Based and Flood Modelling Method. Journal of Environmental Management, 248, Article ID: 109317. https://doi.org/10.1016/j.jenvman.2019.109317
[53]
Camargo, M.E., Roberto Jacobi, P. and Ducrot, R. (2007) Role-Playing Games for Capacity Building in Water and Land Management: Some Brazilian Experiences. Simulation & Gaming, 38, 472-493. https://doi.org/10.1177%2F1046878107300672
[54]
Hogg, M.A. and Vaughan, G.M. (2011) Social Psychology. 6th Edition, Prentice Hall, Hoboken.
[55]
Fiske, S.T. and Taylor, S.E. (2010) Social Cognition. McGraw Hill, New York, 1402.
[56]
Voinov, A. and Bousquet, F. (2010) Modelling with Stakeholders. Environmental Modelling & Software, 25, 1268-1281. https://doi.org/10.1016/j.envsoft.2010.03.007
[57]
Convertino, M., Foran, C.M., Keisler, J.M., Scarlett, L., LoSchiavo, A., Kiker, G.A. and Linkov, I. (2013) Enhanced Adaptive Management: Integrating Decision Analysis, Scenario Analysis and Environmental Modeling for the Everglades. Scientific reports, 3, Article No. 2922. https://doi.org/10.1038/srep02922
Chew, C., Lloyd, G.J. and Knudsen, E. (2013) An Interactive Capacity Building Experience—An Approach with Serious Games. https://www.dhigroup.com/upload/publications/mikebasin/Chew_2013.pdf
[60]
Savic, D.A., Morley, M.S. and Khoury, M. (2016) Serious Gaming for Water Systems Planning and Management. Water, 8, Article No. 456. https://doi.org/10.3390/w8100456
[61]
Di Mauro, A., Cominola, A., Castelletti, A. and Di Nardo, A. (2021) Urban Water Consumption at Multiple Spatial and Temporal Scales. A Review of Existing Datasets. Water, 13, Article No. 36. https://doi.org/10.3390/w13010036
[62]
Creaco, E., Franchini, M. and Walski, T.M. (2016) Comparison of Various Phased Approaches for the Constrained Minimum-Cost Design of Water Distribution Networks. Urban Water Journal, 13, 270-283. https://doi.org/10.1080/1573062X.2014.991330
[63]
Creaco, E., Kossieris, P., Vamvakeridou-Lyroudia, L., Makropoulos, C., Kapelan, Z. and Savic, D. (2016) Parameterizing Residential Water Demand Pulse Models through Smart Meter Readings. Environmental Modelling & Software, 80, 33-40. https://doi.org/10.1016/j.envsoft.2016.02.019
[64]
Sadr, S.M., That, L.T., Ingram, W. and Memon, F.A. (2021) Simulating the Impact of Water Demand Management Options on Water Consumption and Wastewater Generation Profiles. Urban Water Journal, 18, 320-333. https://doi.org/10.1080/1573062X.2021.1893031
[65]
Stewart, R.A., Nguyen, K., Beal, C., Zhang, H., Sahin, O., Bertone, E., Silva, A., Castelletti, A., Cominola, A., Giulianie, M., Giurco, D., Blumenstein, M., Turner, A., Liu, A., Kenway, S., Savic, D., Makropoulos, C. and Kossieris, P. (2018) Integrated Intelligent Water-Energy Metering Systems and Informatics: Visioning a Digital Multi-Utility Service Provider. Environmental Modelling & Software, 105, 94-117. https://doi.org/10.1016/j.envsoft.2018.03.006
[66]
Dahlke, J., Bogner, K., Mueller, M., Berger, T., Pyka, A. and Ebersberger, B. (2020) Is the Juice Worth the Squeeze? Machine Learning (ML) in and for Agent-Based Modelling (ABM). arXiv: 2003.11985.
[67]
Burns, A.J., Posey, C., Courtney, J.F., Roberts, T.L. and Nanayakkara, P. (2017) Organizational Information Security as a Complex Adaptive System: Insights from Three Agent-Based Models. Information Systems Frontiers, 19, 509-524. https://doi.org/10.1007/s10796-015-9608-8
[68]
Giannopoulos, G., Filippini, R. and Schimmer, M. (2012) Risk Assessment Methodologies for Critical Infrastructure Protection. Part I: A State of the Art. Technical notes, EUR 25286 EN, Publications Office of the European Union, Luxembourg.
[69]
Eidson, E.D. and Ehlen, M.A. (2005) NISAC Agent-Based Laboratory for Economics (N-ABLETM): Overview of Agent and Simulation Architectures. Technical Report No. SAND2005-0263, Sandia National Laboratories, Albuquerque.
[70]
Eom, J., Han, Y.-J., Park, S.-H. and Chung, T.-M. (2008) Active Cyber Attack Model for Network System’s Vulnerability Assessment. Proceedings of the 2008 International Conference on Information Science and Security, Seoul, 10-12 January 2008, 153-158. https://doi.org/10.1109/ICISS.2008.36
[71]
Chapman, M., Tyson, G., McBurney, P., Luck, M. and Parsons, S. (2014) Playing Hide-and-Seek: An Abstract Game for Cyber Security. Proceedings of the 1st International Workshop on Agents and CyberSecurity, Paris, 6 May 2014, Article No. 3. https://doi.org/10.1145/2602945.2602946
[72]
Koutiva, I., Moraitis, G. and Makropoulos, C. (2021) An Agent-Based Modelling Approach to Assess Risk in Cyber-Physical Systems (CPS). Accepted in the 17th International Conference on Environmental Science and Technology, Athens, 1-4 September 2021.
[73]
Nikolopoulos, D., Moraitis, G., Bouziotas, D., Lykou, A., Karavokiros, G. and Makropoulos, C. (2020) Cyber-Physical Stress-Testing Platform for Water Distribution Networks. Journal of Environmental Engineering, 146, Article ID: 04020061. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001722
