Energy, Water, Food Nexus Decision-Making for Sustainable …
215
82. Saysel AK, Barlas Y, Yenigün O (2002) Environmental sustainability in an agricultural development project: a system dynamics approach. J Environ Manage. https://doi.org/10.1006/
jema.2001.0488
83. Tsolakis N, Srai JS (2017) A system dynamics approach to food security through smallholder
farming in the UK. Chem Eng Trans. https://doi.org/10.3303/CET1757338
84. Martínez-Jaramillo JE, Arango-Aramburo S, Giraldo-Ramírez DP (2019) The effects of
biofuels on food security: a system dynamics approach for the Colombian case. Sustain
Energy Technol Assess. https://doi.org/10.1016/j.seta.2019.05.009
85. Galli F, Cavicchi A, Brunori G (2019) Food waste reduction and food poverty alleviation: a
system dynamics conceptual model. Agric Hum Values. https://doi.org/10.1007/s10460-01909919-0
86. Martin R, Schlüter M (2015) Combining system dynamics and agent-based modeling to
analyze social-ecological interactions—an example from modeling restoration of a shallow
lake. Front Environ Sci. https://doi.org/10.3389/fenvs.2015.00066
87. Barbati M, Bruno G, Genovese A (2012) Applications of agent-based models for optimization
problems: a literature review. Expert Syst Appl 39(5):6020–6028. https://doi.org/10.1016/j.
eswa.2011.12.015
88. Macal CM, North MJ (2006) Tutorial on agent-based modeling and simulation part 2: how
to model with agents. In: Proceedings—winter simulation conference, pp 73–83. https://doi.
org/10.1109/WSC.2006.323040
89. Wens M et al (2020) Simulating small-scale agricultural adaptation decisions in response to
drought risk: an empirical agent-based model for semi-arid Kenya. Front Water. https://doi.
org/10.3389/frwa.2020.00015
90. Joyita M (2019) Analyzing collaboration in food assistance networks using agent-based
modeling. The University of Texas at Arlington
91. Namany S, Govindan R, Alfagih L, McKay G, Al-Ansari T (2020) Sustainable food security
decision-making: an agent-based modelling approach. J Clean Prod 255:120296. https://doi.
org/10.1016/J.JCLEPRO.2020.120296
92. UN-ISDR (2009) Terminology on disaster risk reduction
93. Namany S, Al-Ansari T, Govindan R (2018) Integrated techno-economic optimization for the
design and operations of energy, water and food nexus systems constrained as non-cooperative
games. Comput Aided Chem Eng 44:1003–1008
94. Basil M, Jamieson A (1999) Uncertainty of complex systems by Monte Carlo simulation.
Meas Control 32(1):16–20. https://doi.org/10.1177/002029409903200104
95. Liu J, Li YP, Huang GH, Zhuang XW, Fu HY (2017) Assessment of uncertainty effects on
crop planning and irrigation water supply using a Monte Carlo simulation based dual-interval
stochastic programming method. J Clean Prod. https://doi.org/10.1016/j.jclepro.2017.02.100
96. Kadigi IL et al (2020) An economic comparison between alternative rice farming systems in
Tanzania using a Monte Carlo simulation approach. Sustainability 12(16):6528. https://doi.
org/10.3390/su12166528
97. Singh A (2019) Foundations of machine learning. SSRN Electron J. https://doi.org/10.2139/
ssrn.3399990
98. Kumar R, Singh MP, Kumar P, Singh JP (2015) Crop selection method to maximize crop
yield rate using machine learning technique. https://doi.org/10.1109/ICSTM.2015.7225403
99. Kuwata K, Shibasaki R (2015) Estimating crop yields with deep learning and remotely sensed
data. https://doi.org/10.1109/IGARSS.2015.7325900
100. Bagheri M, Al-Jabery K, Wunsch D, Burken JG (2020) Examining plant uptake and translocation of emerging contaminants using machine learning: implications to food security. Sci
Total Environ. https://doi.org/10.1016/j.scitotenv.2019.133999
101. Karnib A (2017) Water-energy-food nexus: a coupled simulation and optimization framework.
J Geosci Environ Prot 05(04):84–98. https://doi.org/10.4236/gep.2017.54008
102. Anthony RN (1965) Planning and control: a framework for analysis. Division of Research,
Harvard Business School
103. Storn R (1995) Constrained optimization. Dr Dobb’s J. https://doi.org/10.1201/b18469-7
215
82. Saysel AK, Barlas Y, Yenigün O (2002) Environmental sustainability in an agricultural development project: a system dynamics approach. J Environ Manage. https://doi.org/10.1006/
jema.2001.0488
83. Tsolakis N, Srai JS (2017) A system dynamics approach to food security through smallholder
farming in the UK. Chem Eng Trans. https://doi.org/10.3303/CET1757338
84. Martínez-Jaramillo JE, Arango-Aramburo S, Giraldo-Ramírez DP (2019) The effects of
biofuels on food security: a system dynamics approach for the Colombian case. Sustain
Energy Technol Assess. https://doi.org/10.1016/j.seta.2019.05.009
85. Galli F, Cavicchi A, Brunori G (2019) Food waste reduction and food poverty alleviation: a
system dynamics conceptual model. Agric Hum Values. https://doi.org/10.1007/s10460-01909919-0
86. Martin R, Schlüter M (2015) Combining system dynamics and agent-based modeling to
analyze social-ecological interactions—an example from modeling restoration of a shallow
lake. Front Environ Sci. https://doi.org/10.3389/fenvs.2015.00066
87. Barbati M, Bruno G, Genovese A (2012) Applications of agent-based models for optimization
problems: a literature review. Expert Syst Appl 39(5):6020–6028. https://doi.org/10.1016/j.
eswa.2011.12.015
88. Macal CM, North MJ (2006) Tutorial on agent-based modeling and simulation part 2: how
to model with agents. In: Proceedings—winter simulation conference, pp 73–83. https://doi.
org/10.1109/WSC.2006.323040
89. Wens M et al (2020) Simulating small-scale agricultural adaptation decisions in response to
drought risk: an empirical agent-based model for semi-arid Kenya. Front Water. https://doi.
org/10.3389/frwa.2020.00015
90. Joyita M (2019) Analyzing collaboration in food assistance networks using agent-based
modeling. The University of Texas at Arlington
91. Namany S, Govindan R, Alfagih L, McKay G, Al-Ansari T (2020) Sustainable food security
decision-making: an agent-based modelling approach. J Clean Prod 255:120296. https://doi.
org/10.1016/J.JCLEPRO.2020.120296
92. UN-ISDR (2009) Terminology on disaster risk reduction
93. Namany S, Al-Ansari T, Govindan R (2018) Integrated techno-economic optimization for the
design and operations of energy, water and food nexus systems constrained as non-cooperative
games. Comput Aided Chem Eng 44:1003–1008
94. Basil M, Jamieson A (1999) Uncertainty of complex systems by Monte Carlo simulation.
Meas Control 32(1):16–20. https://doi.org/10.1177/002029409903200104
95. Liu J, Li YP, Huang GH, Zhuang XW, Fu HY (2017) Assessment of uncertainty effects on
crop planning and irrigation water supply using a Monte Carlo simulation based dual-interval
stochastic programming method. J Clean Prod. https://doi.org/10.1016/j.jclepro.2017.02.100
96. Kadigi IL et al (2020) An economic comparison between alternative rice farming systems in
Tanzania using a Monte Carlo simulation approach. Sustainability 12(16):6528. https://doi.
org/10.3390/su12166528
97. Singh A (2019) Foundations of machine learning. SSRN Electron J. https://doi.org/10.2139/
ssrn.3399990
98. Kumar R, Singh MP, Kumar P, Singh JP (2015) Crop selection method to maximize crop
yield rate using machine learning technique. https://doi.org/10.1109/ICSTM.2015.7225403
99. Kuwata K, Shibasaki R (2015) Estimating crop yields with deep learning and remotely sensed
data. https://doi.org/10.1109/IGARSS.2015.7325900
100. Bagheri M, Al-Jabery K, Wunsch D, Burken JG (2020) Examining plant uptake and translocation of emerging contaminants using machine learning: implications to food security. Sci
Total Environ. https://doi.org/10.1016/j.scitotenv.2019.133999
101. Karnib A (2017) Water-energy-food nexus: a coupled simulation and optimization framework.
J Geosci Environ Prot 05(04):84–98. https://doi.org/10.4236/gep.2017.54008
102. Anthony RN (1965) Planning and control: a framework for analysis. Division of Research,
Harvard Business School
103. Storn R (1995) Constrained optimization. Dr Dobb’s J. https://doi.org/10.1201/b18469-7