Technical Papers
Aug 5, 2021

Developing an Agent-Based Model of Dual-Flush Toilet Adoption

Publication: Journal of Water Resources Planning and Management
Volume 147, Issue 10

Abstract

The spread of individual water conservation behaviors within a population can have large impacts on overall water demand. Agent-based models (ABMs) represent individual actors that update their behaviors over time in response to their environment and other agents, and ABMs have been applied to model the adoption of water conservation behaviors and technology. Existing ABM approaches are calibrated based on cumulative water demand data and use assumptions about household-level adoption behaviors. This research develops an ABM of water appliance (dual-flush toilets) adoption and introduces a new approach to calibrate the ABM while allowing for stochasticity and heterogeneity in agent parameters and adoption decisions. The calibration approach uses a noisy genetic algorithm (NGA), and the ABM is calibrated to match household survey data that was collected in Jaipur, India, in 2015. The NGA is applied multiple times to explore variability in the search, and five solutions were found with similar error values. The best-performing solution is applied to project adoption over a 100-year period for varying climate scenarios, and results show quicker adoption rates for dry climates. Sensitivity analysis was conducted for a parameter that represents a delay in adopting dual-flush toilets and a parameter that represents the importance of drought in making adoption decisions. The model presented in this research can be used to aid water resource planning and to anticipate potential impacts of water conservation policies, such as rebate programs or media campaigns.

Get full access to this article

View all available purchase options and get full access to this article.

Data Availability Statement

All survey data used for the model are explored in detail in the study by Ramsey et al. (2017) and are available alongside the source code for the ABM at https://github.com/evramsey/JaipurABM.

Acknowledgments

This work was supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1252376 and the Fulbright–Nehru Student Research Grant, which is administered by the United States Indian Educational Foundation and funded by the Government of India and the United States Government. The authors would like to thank the Centre for Development Communication (CDC), Dr. Rohit Goyal, and local graduate and undergraduate students at the Malaviya Nagar Institute of Technology for conducting surveys used as the basis for this model.

References

Abrahamson, E., and L. Rosenkopf. 1997. “Social network effects on the extent of innovation diffusion: A computer simulation.” Organ. Sci. 8 (3): 289–309. https://doi.org/10.1287/orsc.8.3.289.
Agthe, D. E., and R. B. Billings. 1980. “Dynamic models of residential water demand.” Water Resour. Res. 16 (3): 476–480. https://doi.org/10.1029/WR016i003p00476.
Ajzen, I. 1991. “The theory of planned behavior.” Organ. Behav. Hum. Dec. Process. 50 (2): 179–211. https://doi.org/10.1016/0749-5978(91)90020-T.
Athanasiadis, I. N., and P. A. Mitkas. 2005. “Social influence and water conservation: An agent-based approach.” Comput. Sci. Eng. 7 (1): 65–70.
Balev, S., A. Dutot, Y. Pigne, and G. Savin. 2015. “GraphStream.” Accessed January 31, 2016. https://graphstream-project.org/.
Bass, F. M. 1969. “A new product growth for model consumer durables.” Manage. Sci. 15 (5): 215–227. https://doi.org/10.1287/mnsc.15.5.215.
Berglund, E. Z. 2015. “Using agent-based modeling for water resources planning and management.” J. Water Resour. Plann. Manage. 141 (11): 04015025. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000544.
Bohlmann, J. D., R. J. Calantone, and M. Zhao. 2010. “The effects of market network heterogeneity on innovation diffusion: An agent-based modeling approach.” J. Prod. Innovation Manage. 27: 741–760. https://doi.org/10.1111/j.1540-5885.2010.00748.x.
Byrka, K., A. Je¸drzejewski, K. Sznajd-Weron, and R. Weron. 2016. “Difficulty is critical: The importance of social factors in modeling diffusion of green products and practices.” Renewable Sustainable Energy Rev. 63 (1): 723–735.
Chang, G. 2013. “Factors influencing water conservation behavior among urban residents in China’s arid areas.” Water Policy 15 (5): 691–704. https://doi.org/10.2166/wp.2013.093.
Choudhary, M., R. Sharma, and S. Kumar. 2012. “Development of residential water demand model for a densely populated area of Jaipur City, India.” J. Water Sanit. Hyg. Dev. 2 (1): 10. https://doi.org/10.2166/washdev.2012.029.
Darbandsari, P., R. Kerachian, and S. Malakpour-Estalaki. 2017. “An agent-based behavioral simulation model for residential water demand management: The case-study of Tehran, Iran.” Simul. Modell. Pract. Theory 78 (Nov): 51–72. https://doi.org/10.1016/j.simpat.2017.08.006.
Davis, J. 2004. “Corruption in public service delivery: Experience from South Asia’s water and sanitation sector.” World Dev. 32 (1): 53–71. https://doi.org/10.1016/j.worlddev.2003.07.003.
DeOreo, W. B., and P. W. Mayer. 2012. “Insights into declining single family residential water demands.” J. Am. Water Works Assoc. 104 (6): E383–E394. https://doi.org/10.5942/jawwa.2012.104.0080.
Disaster Management and Relief Department. 2016. “Frequency of drought.” Accessed March 2, 2016. https://www.dmrelief.rajasthan.gov.in/index.php/irrigation-calender/frequency-of-drought.
Edwards, M., N. Ferrand, F. Goreaud, and S. Huet. 2005. “The relevance of aggregating a water consumption model cannot be disconnected from the choice of information available on the resource.” Simul. Modell. Pract. Theory 13 (4): 287–307. https://doi.org/10.1016/j.simpat.2004.11.008.
Fabretti, A. 2013. “On the problem of calibrating an agent based model for financial markets.” J. Econ. Interact. Coordin. 8 (2): 277–293.
Fagiolo, G., M. Guerini, F. Lamperti, A. Moneta, and A. Roventini. 2017. Validation of agent-based models in economics and finance (laboratory of economics and management (LEM) working paper series no. 2017/23). Pisa, Italy: Institute of Economics, Scuola Superiore Sant'Anna. https://doi.org/10.1007/978-3-319-70766-2_31.
Fan, L., G. Liu, F. Wang, V. Geissen, C. J. Ritsema, and Y. Tong. 2013. “Water use patterns and conservation in households of Wei River Basin, China.” Resour. Conserv. Recycl. 74 (May): 45–53. https://doi.org/10.1016/j.resconrec.2013.02.017.
Frost, J. 2014. “Regression analysis: How to interpret S, the standard error of the regression.” Accessed November 20, 2017. https://blog.minitab.com/blog/adventures-in-statistics-2/regression-analysis-how-to-interpret-s-the-standard-error-of-the-regression.
Galán, J. M., A. López-Paredes, and R. del Olmo. 2009. “An agent-based model for domestic water management in Valladolid metropolitan area.” Water Resour. Res. 45 (5): 1–17. https://doi.org/10.1029/2007WR006536.
Gibbs, K. C. 1978. “Price variable in residential water demand models.” Water Resour. Res. 14 (1): 15–18. https://doi.org/10.1029/WR014i001p00015.
Goldberg, D. E. 1989. Genetic algorithms in search, optimization and machine learning. 1st ed. Boston: Addison-Wesley Longman Publishing.
Government of Rajasthan. 2014. “Rainfall data (1957 to 2014).” Accessed August 25, 2015. https://waterresources.rajasthan.gov.in/Daily_Rainfall_Data/Rainfall_Index.htm.
Herrera, M., L. Torgo, J. Izquierdo, and R. Pérez-García. 2010. “Predictive models for forecasting hourly urban water demand.” J. Hydrol. 387 (1–2): 141–150. https://doi.org/10.1016/j.jhydrol.2010.04.005.
Holland, J. 1995. Hidden order: How adaptation builds complexity. New York: Addison-Wesley.
Hoornweg, D., and K. Pope. 2014. Socioeconomic pathways and regional distribution of the world’s 101 largest cities, 143. Nepean, ON: Global Cities Institute.
Jager, W. 2006. “Stimulating the diffusion of photovoltaic systems: A behavioural perspective.” Energy Policy 34 (14): 1935–1943. https://doi.org/10.1016/j.enpol.2004.12.022.
Jaipur Development Authority. 2011. “Master development plan-2025.” Accessed August 1, 2014. https://www.jaipurjda.org/pdf/MDP/Vol2.pdf.
Jing, L., B. Chen, B. Zhang, and X. Ye. 2018. “Modeling marine oily wastewater treatment by a probabilistic agent-based approach.” Mar. Pollut. Bull. 127 (Jun): 217–224. https://doi.org/10.1016/j.marpolbul.2017.12.004.
Kandiah, V., A. R. Binder, and E. Z. Berglund. 2017. “An empirical agent-based model to simulate the adoption of water reuse using the social amplification of risk framework.” Risk Anal. 37 (10): 2005–2022. https://doi.org/10.1111/risa.12760.
Kandiah, V. K., E. Z. Berglund, and A. R. Binder. 2016. “Cellular automata modeling framework for urban water reuse planning and management.” J. Water Resour. Plann. Manage. 142 (12): 04016054. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000696.
Kandiah, V. K., E. Z. Berglund, and A. R. Binder. 2019. “An agent-based modeling approach to project adoption of water reuse and evaluate expansion plans within a sociotechnical water infrastructure system.” Sustainable Cities Soc. 46 (Nov): 101412. https://doi.org/10.1016/j.scs.2018.12.040.
Kiesling, E., M. Günther, C. Stummer, and L. M. Wakolbinger. 2012. “Agent-based simulation of innovation diffusion: A review.” Cent. Eur. J. Oper. Res. 20 (2): 183–230. https://doi.org/10.1007/s10100-011-0210-y.
Koutiva, I., and C. Makropoulos. 2016. “Modelling domestic water demand: An agent based approach.” Environ. Modell. Software 79 (May): 35–54. https://doi.org/10.1016/j.envsoft.2016.01.005.
Koutiva, I., and C. Makropoulos. 2017. “Exploring the effects of domestic water management measures to water conservation attitudes using agent based modelling.” Water Sci. Technol. Water Supply 17 (2): 552–560. https://doi.org/10.2166/ws.2016.161.
Krause, P., D. P. Boyle, and F. Bäse. 2005. “Comparison of different efficiency criteria for hydrological model assessment.” Adv. Geosci. 5 (Dec): 89–97. https://doi.org/10.5194/adgeo-5-89-2005.
Lamperti, F., A. Roventini, and A. Sani. 2018. “Agent-based model calibration using machine learning surrogates.” J. Econ. Dyn. Control 90 (May): 366–389. https://doi.org/10.1016/j.jedc.2018.03.011.
Legates, D. R., and G. J. McCabe. 1999. “Evaluating the use of ‘goodness-of-fit’ measures in hydrologic and hydroclimatic model validation.” Water Resour. Res. 35 (1): 233–241. https://doi.org/10.1029/1998WR900018.
Luke, S. et al. 2004. “Mason: A new multi-agent simulation toolkit.” In Vol. 8 of Proc., 2004 SwarmFest Workshop. Ann Arbor, MI: Univ. of Michigan.
Luo, Q., J. Wu, Y. Yang, J. Qian, and J. Wu. 2014. “Optimal design of groundwater remediation system using a probabilistic multi-objective fast harmony search algorithm under uncertainty.” J. Hydrol. 519 (Nov): 3305–3315. https://doi.org/10.1016/j.jhydrol.2014.10.023.
Luo, Q., J. Wu, Y. Yang, J. Qian, and J. Wu. 2016. “Multi-objective optimization of long-term groundwater monitoring network design using a probabilistic Pareto genetic algorithm under uncertainty.” J. Hydrol. 534 (Mar): 352–363. https://doi.org/10.1016/j.jhydrol.2016.01.009.
McKenzie-Mohr, D., and W. Smith. 1999. Fostering sustainable behavior: An introduction to community-based social marketing. Gabriola Island, BC, Canada: New Society Publishers.
Midgley, D. F., P. D. Morrison, and J. H. Roberts. 1992. “The effect of network structure in industrial diffusion processes.” Res. Policy 21 (6): 533–552. https://doi.org/10.1016/0048-7333(92)90009-S.
Miller, B. L., and D. E. Goldberg. 1996. “Optimal sampling for genetic algorithms.” In Vol. 6 of Proc., Intelligent Engineering Systems through Artificial Neural Networks (ANNIE 96). Edited by C. H. Dagli, M. Akay, C. L. P. Chan, B. R. Fernandez, and J. Ghosh, 291–298. New York: ASME.
Miller, J. H., and S. E. Page. 2007. Complex adaptive systems. Princeton, NJ: Princeton University Press.
Müller, B., F. Bohn, G. Dreßler, J. Groeneveld, C. Klassert, R. Martin, M. Schlüter, J. Schulze, H. Weise, and N. Schwarz. 2013. “Describing human decisions in agent-based models - ODD+D, an extension of the ODD protocol.” Environ. Modell. Software 48 (Oct): 37–48. https://doi.org/10.1016/j.envsoft.2013.06.003.
Nash, J. E., and J. V. Sutcliffe. 1970. “River flow forecasting through conceptual models. Part I—A discussion of principles.” J. Hydrol. 10 (1): 282–290.
Nicklow, J., et al. 2010. “State of the art for genetic algorithms and beyond in water resources planning and management.” J. Water Resour. Plann. Manage. 136 (4): 412–432. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000053.
Office of the Registrar General & Census Commissioner. 2011a. “Annual health survey 2011-12 fact sheet.” https://www.censusindia.gov.in/vital_statistics/AHSBulletins/AHS_Factsheets_2011_12/Rajasthan_Factsheet_2011-12.pdf.
Office of the Registrar General & Census Commissioner, I. 2011b. “Census of India 2011.” Accessed July 1, 2015. http://www.censusindia.gov.in/pca/Searchdata.aspx.
Oloo, F., and G. Wallentin. 2017. “An adaptive agent-based model of homing pigeons: A genetic algorithm approach.” Int. J. Geo-Inf. 6 (1): 27: https://doi.org/10.3390/ijgi6010027.
Ramsey, E. 2006. “Jaipur survey results.” Github. Accessed July 7, 2021. https://github.com/evramsey/JaipurABM/blob/master/JaipurSurveyResults.xlsx.
Ramsey, E., E. Z. Berglund, and R. Goyal. 2017. “The impact of demographic factors, beliefs, and social influences on residential water consumption and implications for non-price policies in urban India.” Water 9 (11): 844. https://doi.org/10.3390/w9110844.
Renwick, M. E., and S. O. Archibald. 1998. “Demand side management policies for residential water use: Who bears the conservation burden?” Land Econ. 74 (3): 343–359. https://doi.org/10.2307/3147117.
Rogers, E. M. 1962. Diffusion of innovations. New York: Free Press of Glenscoe.
Rosenberg, D. E. 2015. “Blended near-optimal alternative generation, visualization, and interaction for water resources decision making.” Water Resour. Res. 51 (4): 2047–2063. https://doi.org/10.1002/2013WR014667.
Schwarz, N., and A. Ernst. 2009. “Agent-based modeling of the diffusion of environmental innovations—An empirical approach.” Technol. Forecasting Soc. Change 76 (4): 497–511. https://doi.org/10.1016/j.techfore.2008.03.024.
Shafiee, M. E., and E. Z. Berglund. 2015. “Real-time guidance for hydrant flushing using sensor-hydrant decision trees.” J. Water Resour. Plann. Manage. 141 (6): 04014079. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000475.
Shakya, H. B., N. A. Christakis, and J. H. Fowler. 2014. “Association between social network communities and health behavior: An observational sociocentric network study of latrine ownership in rural India.” Am. J. Public Health 104 (5): 930–937. https://doi.org/10.2105/AJPH.2013.301811.
Singh, A. 2012. “‘Damned’ Ramgarh still dry.” Accessed July 1, 2014. https://timesofindia.indiatimes.com/city/jaipur/Damned-ramgarh-still-dry/articleshow/15626741.cms.
Smalley, J., and B. Minsker. 2000. “Risk-based in situ bioremediation design using a noisy genetic algorithm.” Water Resour. Res. 36 (10): 3043–3052. https://doi.org/10.1029/2000WR900191.
Sornette, D. 2014. “Physics and financial economics (1776-2014): Puzzles, Ising and agent-based models.” Rep. Prog. Phys. 77 (6): 062001. https://doi.org/10.1088/0034-4885/77/6/062001.
Times of India. 2015. “Jaipur: Groundwater table sinks 25 metres in 10 yrs.” Accessed June 1, 2016. https://timesofindia.indiatimes.com/city/jaipur/Jaipur-Groundwater-table-sinks-25-metres-in-10-yrs/articleshow/47227708.cms.
USGS. 2002. Estimating water use in the United States: A new paradigm for the national water-use information program. Washington, DC: National Research Council. https://doi.org/10.17226/10484.
Valente, T. W. 1996. “Social network thresholds in the diffusion of innovations.” Social Network 18 (1): 69–89. https://doi.org/10.1016/0378-8733(95)00256-1.
Voloshin, D., D. Rybokonenko, and V. Karbovskii. 2015. “Optimization-based calibration for micro-level agent-based simulation of pedestrian behavior in public spaces.” Procedia Comput. Sci. 66 (Jan): 372–381. https://doi.org/10.1016/j.procs.2015.11.043.
Weisbuch, G., H. Gutowitz, and G. Duchateau-Nguyen. 1996. “Information contagion and the economics of pollution.” J. Econ. Behav. Organ. 29 (3): 389–407. https://doi.org/10.1016/0167-2681(95)00079-8.
Willis, R., R. A. Stewart, K. Panuwatwanich, B. Capati, and D. Giurco. 2009. “Gold coast domestic water end use study.” Water: J. Aust. Water Assoc. 36 (6): 79e85.
Willis, R. M., R. A. Stewart, K. Panuwatwanich, P. R. Williams, and A. L. Hollingsworth. 2011. “Quantifying the influence of environmental and water conservation attitudes on household end use water consumption.” J. Environ. Manage. 92 (8): 1996–2009. https://doi.org/10.1016/j.jenvman.2011.03.023.
Windrum, P., G. Fagiolo, and A. Moneta. 2007. “Empirical validation of agent-based models: Alternatives and prospects.” J. Artif. Soc. Soc. Simul. 10 (2): 8.
Wu, J., C. Zheng, C. Chien, and L. Zheng. 2006. “A comparative study of Monte Carlo simple genetic algorithm and noisy genetic algorithm for cost-effective sampling network design under uncertainty.” Adv. Water Resour. 29 (6): 899–911. https://doi.org/10.1016/j.advwatres.2005.08.005.
Yan, S., and B. Minkser. 2011. “Applying dynamic surrogate models in noisy genetic algorithms to optimize groundwater remediation designs.” J. Water Resour. Plann. Manage. 137 (3): 284–292. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000106.
Young, P. 1999. Diffusion in social networks. Working Paper Rep. No 2. Washington, DC: Brookings Institution.
Young, R. A. 1973. “Price elasticity of demand for municipal water: A case study of Tucson, Arizona.” Water Resour. Res. 9 (4): 1068–1072. https://doi.org/10.1029/WR009i004p01068.
Zechman, E. M., and S. R. Ranjithan. 2004. “An evolutionary algorithm to generate alternatives (EAGA) for engineering optimization problems.” Eng. Optim. 36 (5): 539–553. https://doi.org/10.1080/03052150410001704863.
Zhang, H., and Y. Vorobeychik. 2019. “Empirically grounded agent-based models of innovation diffusion: A critical review.” Artif. Intell. Rev. 52 (1): 707–741. https://doi.org/10.1007/s10462-017-9577-z.
Zhang, H. H., and D. F. Brown. 2005. “Understanding urban residential water use in Beijing and Tianjin, China.” Habitat Int. 29 (3): 469–491. https://doi.org/10.1016/j.habitatint.2004.04.002.
Zhuge, C., C. Shao, J. Gao, C. Dong, and H. Zhang. 2016. “Agent-based joint model of residential location choice and real estate price for land use and transport model.” Comput. Environ. Urban Syst. 57 (May): 93–105. https://doi.org/10.1016/j.compenvurbsys.2016.02.001.

Information & Authors

Information

Published In

Go to Journal of Water Resources Planning and Management
Journal of Water Resources Planning and Management
Volume 147Issue 10October 2021

History

Received: Jul 10, 2020
Accepted: May 24, 2021
Published online: Aug 5, 2021
Published in print: Oct 1, 2021
Discussion open until: Jan 5, 2022

Permissions

Request permissions for this article.

Authors

Affiliations

Graduate Research Assistant, Dept. of Civil, Construction, and Environmental Engineering, North Carolina State Univ., 3331 Fitts-Woolard Hall, 915 Partners Way, Raleigh, NC 27695 (corresponding author). ORCID: https://orcid.org/0000-0002-8704-633X. Email: [email protected]
E. Z. Berglund, M.ASCE
Professor, Dept. of Civil, Construction, and Environmental Engineering, North Carolina State Univ., 3169 Fitts-Woolard Hall, 915 Partners Way, Raleigh, NC 27695.

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

Cited by

  • Modeling and Validation of Residential Water Demand in Agent-Based Models: A Systematic Literature Review, Water, 10.3390/w15030579, 15, 3, (579), (2023).
  • Introducing a Novel Hybrid Agent-Based Framework for Simulating the Adoption of Residential Water Conservation Behaviors, World Environmental and Water Resources Congress 2023, 10.1061/9780784484852.073, (775-788), (2023).

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share