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Radial interval chance-constrained programming for agricultural non-point source water pollution control under uncertainty

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  • Tan, Q.
  • Huang, G.H.
  • Cai, Y.P.

Abstract

Inherent uncertainties in agricultural non-point source water pollution control problems cause great difficulties in relevant modeling processes. A radial interval chance-constrained programming (RICCP) approach was developed in this study for supporting source-oriented non-point source pollution control under uncertainty. The proposed RICCP approach could tackle two-layer uncertainty resulting from temporal and spatial variability of many factors and their uncertain interactions. Based on the concept of radial intervals and chance-constrained programming, RICCP could reflect the randomness in the bounds of interval parameters, with or without known probability distributions. RICCP could also allow decision makers to adjust the conservativeness of solutions via protection and significance levels, helping satisfy environmental, economic and resource-conservation requirements in a holistic and interactive manner. The proposed methodology has been applied to an agricultural water pollution control case. The most-profit agricultural development strategies were explored while restricting environmental impacts to an acceptable level. A series of interval solutions for agricultural practices were generated corresponding to varied risk levels of constraint violations, which could help screen optimal alternatives according to decision makers' profit and risk considerations as well as various system conditions. RICCP model was also compared to its alternatives. Significant differences in the solutions among the compared models further demonstrated the advantages of the proposed approach.

Suggested Citation

  • Tan, Q. & Huang, G.H. & Cai, Y.P., 2011. "Radial interval chance-constrained programming for agricultural non-point source water pollution control under uncertainty," Agricultural Water Management, Elsevier, vol. 98(10), pages 1595-1606, August.
    • Handle: RePEc:eee:agiwat:v:98:y:2011:i:10:p:1595-1606
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    2. Cai, Yanpeng & Applegate, Scott & Yue, Wencong & Cai, Jianying & Wang, Xuan & Liu, Gengyuan & Li, Chunhui, 2017. "A hybrid life cycle and multi-criteria decision analysis approach for identifying sustainable development strategies of Beijing's taxi fleet," Energy Policy, Elsevier, vol. 100(C), pages 314-325.
    3. Zhang, Y.M. & Lu, H.W. & Nie, X.H. & He, L. & Du, P., 2014. "An interactive inexact fuzzy bounded programming approach for agricultural water quality management," Agricultural Water Management, Elsevier, vol. 133(C), pages 104-111.
    4. Shen, Z.Y. & Chen, L. & Liao, Q. & Liu, R.M. & Huang, Q., 2013. "A comprehensive study of the effect of GIS data on hydrology and non-point source pollution modeling," Agricultural Water Management, Elsevier, vol. 118(C), pages 93-102.
    5. Wu, Xin & Zheng, Yi & Wu, Bin & Tian, Yong & Han, Feng & Zheng, Chunmiao, 2016. "Optimizing conjunctive use of surface water and groundwater for irrigation to address human-nature water conflicts: A surrogate modeling approach," Agricultural Water Management, Elsevier, vol. 163(C), pages 380-392.
    6. C. Dai & Y. P. Cai & W. T. Lu & H. Liu & H. C. Guo, 2016. "Conjunctive Water Use Optimization for Watershed-Lake Water Distribution System under Uncertainty: a Case Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4429-4449, September.
    7. Akbari, Fatemeh & Shourian, Mojtaba & Moridi, Ali, 2022. "Assessment of the climate change impacts on the watershed-scale optimal crop pattern using a surface-groundwater interaction hydro-agronomic model," Agricultural Water Management, Elsevier, vol. 265(C).
    8. Dai, C. & Cai, Y.P. & Ren, W. & Xie, Y.F. & Guo, H.C., 2016. "Identification of optimal placements of best management practices through an interval-fuzzy possibilistic programming model," Agricultural Water Management, Elsevier, vol. 165(C), pages 108-121.
    9. Liu, M. & Huang, G.H. & Liao, R.F. & Li, Y.P. & Xie, Y.L., 2013. "Fuzzy two-stage non-point source pollution management model for agricultural systems—A case study for the Lake Tai Basin, China," Agricultural Water Management, Elsevier, vol. 121(C), pages 27-41.
    10. Ji, L. & Niu, D.X. & Huang, G.H., 2014. "An inexact two-stage stochastic robust programming for residential micro-grid management-based on random demand," Energy, Elsevier, vol. 67(C), pages 186-199.
    11. Cai, Yanpeng & Yue, Wencong & Xu, Linyu & Yang, Zhifeng & Rong, Qiangqiang, 2016. "Sustainable urban water resources management considering life-cycle environmental impacts of water utilization under uncertainty," Resources, Conservation & Recycling, Elsevier, vol. 108(C), pages 21-40.
    12. Li, Zhong & Huang, Gordon & Zhang, Yimei & Li, Yongping, 2013. "Inexact two-stage stochastic credibility constrained programming for water quality management," Resources, Conservation & Recycling, Elsevier, vol. 73(C), pages 122-132.

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