An inexact risk management model for agricultural land-use planning under water shortage

Wei LI , Changchun FENG , Chao DAI , Yongping LI , Chunhui LI , Ming LIU

Front. Earth Sci. ›› 2016, Vol. 10 ›› Issue (3) : 419 -431.

PDF (604KB)
Front. Earth Sci. ›› 2016, Vol. 10 ›› Issue (3) : 419 -431. DOI: 10.1007/s11707-015-0544-1
RESEARCH ARTICLE
RESEARCH ARTICLE

An inexact risk management model for agricultural land-use planning under water shortage

Author information +
History +
PDF (604KB)

Abstract

Water resources availability has a significant impact on agricultural land-use planning, especially in a water shortage area such as North China. The random nature of available water resources and other uncertainties in an agricultural system present risk for land-use planning and may lead to undesirable decisions or potential economic loss. In this study, an inexact risk management model (IRM) was developed for supporting agricultural land-use planning and risk analysis under water shortage. The IRM model was formulated through incorporating a conditional value-at-risk (CVaR) constraint into an inexact two-stage stochastic programming (ITSP) framework, and could be used to control uncertainties expressed as not only probability distributions but also as discrete intervals. The measure of risk about the second-stage penalty cost was incorporated into the model so that the trade-off between system benefit and extreme expected loss could be analyzed. The developed model was applied to a case study in the Zhangweinan River Basin, a typical agricultural region facing serious water shortage in North China. Solutions of the IRM model showed that the obtained first-stage land-use target values could be used to reflect decision-makers’ opinions on the long-term development plan. The confidence level α and maximum acceptable risk loss β could be used to reflect decision-makers’ preference towards system benefit and risk control. The results indicated that the IRM model was useful for reflecting the decision-makers’ attitudes toward risk aversion and could help seek cost-effective agricultural land-use planning strategies under complex uncertainties.

Keywords

agricultural land-use planning / risk management / CVaR / uncertainty / water shortage

Cite this article

Download citation ▾
Wei LI, Changchun FENG, Chao DAI, Yongping LI, Chunhui LI, Ming LIU. An inexact risk management model for agricultural land-use planning under water shortage. Front. Earth Sci., 2016, 10(3): 419-431 DOI:10.1007/s11707-015-0544-1

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Andersson F, Mausser H, Rosen D, Uryasev S (2001). Credit risk optimization with conditional value-at-risk criterion. Math Program, 89(2): 273–291

[2]

Carneiro M C, Ribas G P, Hamacher S (2010). Risk management in the oil supply chain: a CVaR approach. Ind Eng Chem Res, 49(7): 3286–3294

[3]

Chang N B, Wen C G, Chen Y L, Yong Y C (1996). A grey fuzzy multi-objective programming approach for the optimal planning of a reservoir watershed, part A: theoretical development. Water Res, 30(10): 2329–2334

[4]

DRIZRA (Design and Research Institute of Zhangweinan River Administration) (2008). Management Policy and Operation Regulation Report of Yuecheng Reservoir

[5]

EBCZR (Editorial Board of Chorography of Zhangweinan River) (2003). Chorography of Zhangweinan River. Tianjin: Tianjin Science & Technology Press

[6]

El-Shishiny H (1988). A goal programming model for planning the development of newly reclaimed lands. Agric Syst, 26(4): 245–261

[7]

Glen J J, Tipper R (2001). A mathematical programming model for improvement planning in a semi-subsistence farm. Agric Syst, 70(1): 295–317

[8]

Guo P, Huang G H, He L (2008). ISMISIP: an inexact stochastic mixed integer linear semi-infinite programming approach for solid waste management and planning under uncertainty. Stochastic Environ Res Risk Assess, 22(6): 759–775

[9]

Huang G H (1996). IPWM: an interval parameter water quality management model. Eng Optim, 26(2): 79–103

[10]

Huang G H, Baetz B W, Patry G G (1994). Capacity planning for municipal solid waste management systems under uncertainty–a grey fuzzy dynamic programming (GFDP) approach. Journal of Urban Planning and Development 120: 132–156

[11]

Huang G H, Li Y P, Xiao H N, Qin X S (2007). An inexact two-stage quadratic program for water resources planning. Journal of Environmental Informatics, 10(2): 99–105

[12]

Huang G H, Loucks D P (2000). An inexact two-stage stochastic programming model for water resources management under uncertainty. Civ Eng Environ Syst, 17(2): 95–118

[13]

Huang Y, Chen X, Li Y P, Bao A M, Ma Y G (2012). A simulation-based two-stage interval-stochastic programming model for water resources management in Kaidu-Konqi watershed, China. Journal of Arid Land, 4(4): 390–398

[14]

Kira D, Kusy M, Rakita I (1997). A stochastic linear programming approach to hierarchical production planning. J Oper Res Soc, 48(2): 207–211

[15]

Li W, Li Y P, Li C H, Huang G H (2010). An inexact two-stage water management model for planning agricultural irrigation under uncertainty. Agric Water Manage, 97(11): 1905–1914

[16]

Li Y P, Huang G H (2008). Interval-parameter two-stage stochastic nonlinear programming for water resources management under uncertainty. Water Resour Manage, 22(6): 681–698

[17]

Li Y P, Huang G H (2009). Two-stage planning for sustainable water quality management under uncertainty. J Environ Manage, 90(8): 2402–2413

[18]

Li Y P, Huang G H, Nie S L (2006). An interval-parameter multi-stage stochastic programming model for water resources management under uncertainty. Adv Water Resour, 29(5): 776–789

[19]

Li Y P, Li W, Huang G H (2012). Two-stage inexact-probabilistic programming model for water quality management. Environ Eng Sci, 29(7): 1–13

[20]

Li Z, Huang G H, Zhang Y M, Li Y P (2013). Inexact two-stage stochastic credibility constrained programming for water quality management. Resour Conserv Recycling, 73: 122–132

[21]

Liu Y, Lv X J, Qin X S, Guo H C, Yu Y J, Mao G Z (2007). An integrated GIS-based analysis system for land-use management of lake areas in urban fringe. Landsc Urban Plan, 82(4): 233–246

[22]

Lu H W, Huang G H, Zhang Y M, He L (2012). Strategic agricultural land-use planning in response to water-supplier variation in a China’s rural region. Agric Syst, 108: 19–28

[23]

Maqsood I, Huang G H, Yeomans J S (2005). An interval-parameter fuzzy two-stage stochastic program for water resources management under uncertainty. Eur J Oper Res, 167(1): 208–225

[24]

Noyan N (2012). Risk-averse two-stage stochastic programming with an application to disaster management. Comput Oper Res, 39(3): 541–559

[25]

Piantadosi J, Metcalfe A V, Howlett P G (2008). Stochastic dynamic programming (SDP) with a conditional value-at-risk (CVaR) criterion for management of storm-water. J Hydrol (Amst), 348(3–4): 320–329

[26]

Qin X S, Huang G H, Zeng G M, Chakma A, Huang Y F (2007). An interval-parameter fuzzy nonlinear optimization model for stream water quality management under uncertainty. Eur J Oper Res, 180(3): 1331–1357

[27]

Raju K S, Kumar D N (1999). Multi-criterion decision making in irrigation planning. Agric Syst, 62(2): 117–129

[28]

Rockafellar R T, Uryasev S (2000). Optimization of conditional value-at-risk. Journal of Risk, 2(3): 21–41

[29]

Rockafellar R T, Uryasev S (2002). Conditional value-at-risk for general loss distributions. J Bank Finance, 26(7): 1443–1471

[30]

Russell S O, Campbell P F (1996). Reservoir operating rules with fuzzy programming. J Water Resour Plan Manage, 122(3): 165–170

[31]

Shakya K M, Leuschner W A (1990). A multiple objective land use planning model for Nepalese hills farms. Agric Syst, 34(2): 133–149

[32]

Shao L G, Qin X S, Xu Y (2011). A conditional value-at-risk based inexact water allocation model. Water Resour Manage, 25(9): 2125–2145

[33]

Suo M Q, Li Y P, Huang G H (2011). An inventory-theory-based interval-parameter two-stage stochastic programming model for water resources management. Eng Optim, 43(9): 999–1018

[34]

Wagner J M, Shamir U, Marks D H (1994). Containing groundwater contamination: planning models using stochastic programming with recourse. Eur J Oper Res, 77(1): 1–26

[35]

Webby R B, Adamson P T, Boland J, Howlett P G, Metcalfe A V, Piantadosi J (2007). The Mekong-applications of value at risk (VaR) and conditional value at risk (CVaR) simulation to the benefits, costs and consequences of water resources development in a large river basin. Ecol Modell, 201(1): 89–96

[36]

Xu Y, Qin X S (2010). Agricultural effluent control under uncertainty: an inexact double-sided fuzzy chance constrained model. Adv Water Resour, 33(9): 997–1014

[37]

Yamout G M, Hatfield K, Romeijn HE (2007). Comparison of new conditional value-at-risk-based management models for optimal allocation of uncertain water supplies. Water Resources Research, 43(7): W07430

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag Berlin Heidelberg

AI Summary AI Mindmap
PDF (604KB)

Supplementary files

FES-15544-OF-FCC_suppl_1

999

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/