IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v34y2020i5d10.1007_s11269-019-02422-5.html
   My bibliography  Save this article

Fractional Stochastic Interval Programming for Optimal Low Impact Development Facility Category Selection under Uncertainty

Author

Listed:
  • Jinjin Gu

    (Hefei University of Technology)

  • Hui Hu

    (Hefei University of Technology)

  • Lin Wang

    (Hefei University of Technology)

  • Wei Xuan

    (Hefei University of Technology)

  • Yuan Cao

    (Hefei University of Technology)

Abstract

Uncertainties in nature and human society influence low impact development (LID) facility category selection during LID facility optimization distribution, however the investigation of this area is seldom. There are still two problems with uncertainty which influence LID facility distribution 1) how uncertainty factors affect LID facility selection and 2) in the case of a number of LID facilities of multiple categories are to be set, how to construct the LID facility optimization distribution model for LID facility category selection under uncertainty. To handle the problems, this study develops a fractional stochastic interval programming model to process LID facility category selection under the influence of uncertainty. The model can either process multiple objectives via objective maximization and minimization or process the stochastic uncertainty and interval uncertainty. The study shows that the uncertainties which influence LID facility category selection exist in rainfall, infiltration rate, release coefficient, unit price and budget. and the study reveal that the key constraint of LID facility category selection is the uncertainty parameter characteristic of the LID facility, in which different parameters lead to various LID facility optimization schemes. Results of the model include a series of LID facility optimization distribution schemes in multiple scenarios.Results also provide a series of feasible schemes for decision makers, and the manager can select the most appropriate scheme according to water processing level or budget. The developed model could 1) identifying the uncertainty which impact the LID facility distribution. 2) processing the LID facility category selection under interval uncertainty and stochastic uncertainty during LID facility optimization distribution. The method can also be used to estimate the rationality of the LID facility optimization scheme. Moreover, the proposed method is universal and could be extended to other cases of LID facility category selection under uncertainty.

Suggested Citation

  • Jinjin Gu & Hui Hu & Lin Wang & Wei Xuan & Yuan Cao, 2020. "Fractional Stochastic Interval Programming for Optimal Low Impact Development Facility Category Selection under Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(5), pages 1567-1587, March.
  • Handle: RePEc:spr:waterr:v:34:y:2020:i:5:d:10.1007_s11269-019-02422-5
    DOI: 10.1007/s11269-019-02422-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-019-02422-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11269-019-02422-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. G. H. Huang & B. W. Baetz & G. G. Patry, 1998. "Trash-Flow Allocation: Planning Under Uncertainty," Interfaces, INFORMS, vol. 28(6), pages 36-55, December.
    2. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
    3. Jinjin Gu & Quan Zhang & Dazhi Gu & Qingguo Zhang & Xiao Pu, 2018. "The Impact of Uncertainty Factors on Optimal Sizing and Costs of Low-Impact Development: a Case Study from Beijing, China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(13), pages 4217-4238, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gu Jinjin & Lyu Xiaoqian & Fang Buyun & Hui Qiang & Cao Yuan, 2023. "Study on Planning and Design of Blue-Green-Gray Transformation of Lakeside Cities to Deal with the Complex Urban Waterlogging Caused by Extreme Rainstorm," Land, MDPI, vol. 12(2), pages 1-16, January.
    2. Jinjin Gu & Yuan Cao & Min Wu & Min Song & Lin Wang, 2022. "A Novel Method for Watershed Best Management Practices Spatial Optimal Layout under Uncertainty," Sustainability, MDPI, vol. 14(20), pages 1-18, October.
    3. Rafael González-Val, 2021. "The Probability Distribution of Worldwide Forest Areas," Sustainability, MDPI, vol. 13(3), pages 1-19, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, S. & Huang, G.H., 2014. "An integrated approach for water resources decision making under interactive and compound uncertainties," Omega, Elsevier, vol. 44(C), pages 32-40.
    2. Salman Sharifazari & Shahab Araghinejad, 2015. "Development of a Nonparametric Model for Multivariate Hydrological Monthly Series Simulation Considering Climate Change Impacts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5309-5322, November.
    3. Mohammad Zounemat-Kermani, 2016. "Investigating Chaos and Nonlinear Forecasting in Short Term and Mid-term River Discharge," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1851-1865, March.
    4. Quande Qin & Huangda He & Li Li & Ling-Yun He, 2020. "A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1249-1273, April.
    5. Vidhi Vig & Anmol Kaur, 2022. "Time series forecasting and mathematical modeling of COVID-19 pandemic in India: a developing country struggling to cope up," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 2920-2933, December.
    6. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Lin Qiu & Can-can Liu, 2017. "The Annual Maximum Flood Peak Discharge Forecasting Using Hermite Projection Pursuit Regression with SSO and LS Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 461-477, January.
    7. Baoying Shan & Ping Guo & Shanshan Guo & Zhong Li, 2019. "A Price-Forecast-Based Irrigation Scheduling Optimization Model under the Response of Fruit Quality and Price to Water," Sustainability, MDPI, vol. 11(7), pages 1-21, April.
    8. Liangxu Liu & Xueyong Zhao & Qinglan Meng & He Zhao & Xiaoqian Lu & Junkai Gao & Xueli Chang, 2017. "Annual Precipitation Fluctuation and Spatial Differentiation Characteristics of the Horqin Region," Sustainability, MDPI, vol. 9(1), pages 1-16, January.
    9. Ali Danandeh Mehr & Vahid Nourani, 2018. "Season Algorithm-Multigene Genetic Programming: A New Approach for Rainfall-Runoff Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(8), pages 2665-2679, June.
    10. Fu Qiao, 2020. "Study on Price Fluctuation of Industry Index in Chinas Stock Market Based on Empirical Mode Decomposition," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 10(5), pages 559-573, May.
    11. Parisa-Sadat Ashofteh & Taher Rajaee & Parvin Golfam, 2017. "Assessment of Water Resources Development Projects under Conditions of Climate Change Using Efficiency Indexes (EIs)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(12), pages 3723-3744, September.
    12. Ervin Shan Khai Tiu & Yuk Feng Huang & Jing Lin Ng & Nouar AlDahoul & Ali Najah Ahmed & Ahmed Elshafie, 2022. "An evaluation of various data pre-processing techniques with machine learning models for water level prediction," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(1), pages 121-153, January.
    13. Zhong-kai Feng & Wen-jing Niu & Zhi-qiang Jiang & Hui Qin & Zhen-guo Song, 2020. "Monthly Operation Optimization of Cascade Hydropower Reservoirs with Dynamic Programming and Latin Hypercube Sampling for Dimensionality Reduction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(6), pages 2029-2041, April.
    14. Fan, Xinying, 2022. "A method for the generation of typical meteorological year data using ensemble empirical mode decomposition for different climates of China and performance comparison analysis," Energy, Elsevier, vol. 240(C).
    15. Zong-chang Yang, 2018. "Predictive Modeling of Hourly Water-Level Fluctuations Based on the DCT Least-Squares Extended Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(3), pages 1117-1131, February.
    16. Peng Chen & Andrew Vivian & Cheng Ye, 2022. "Forecasting carbon futures price: a hybrid method incorporating fuzzy entropy and extreme learning machine," Annals of Operations Research, Springer, vol. 313(1), pages 559-601, June.
    17. Emanuele Ogliari & Alfredo Nespoli & Marco Mussetta & Silvia Pretto & Andrea Zimbardo & Nicholas Bonfanti & Manuele Aufiero, 2020. "A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network," Forecasting, MDPI, vol. 2(4), pages 1-19, October.
    18. Yun Bai & Nejc Bezak & Klaudija Sapač & Mateja Klun & Jin Zhang, 2019. "Short-Term Streamflow Forecasting Using the Feature-Enhanced Regression Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4783-4797, November.
    19. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing, 2017. "Multi-step ahead wind speed forecasting using an improved wavelet neural network combining variational mode decomposition and phase space reconstruction," Renewable Energy, Elsevier, vol. 113(C), pages 1345-1358.
    20. H. Lu & G. Huang & Y. Lin & L. He, 2009. "A Two-Step Infinite α-Cuts Fuzzy Linear Programming Method in Determination of Optimal Allocation Strategies in Agricultural Irrigation Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(11), pages 2249-2269, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:34:y:2020:i:5:d:10.1007_s11269-019-02422-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.