Inexact nonlinear improved fuzzy chance-constrained programming model for irrigation water management under uncertainty

•An inexact nonlinear mλ-measure fuzzy chance-constrained programming model is developed.•The model is applied to Heihe River Basin in northwest China for irrigation water management.•Interval quadratic crop water production functions are obtained based on interval regression method.•The model impro...

Full description

Saved in:
Bibliographic Details
Published in:Journal of hydrology (Amsterdam) Vol. 556; pp. 397 - 408
Main Authors: Zhang, Chenglong, Zhang, Fan, Guo, Shanshan, Liu, Xiao, Guo, Ping
Format: Journal Article
Language:English
Published: Elsevier B.V 01.01.2018
Subjects:
case studies
China
Interval crop water production function
irrigation management
irrigation water
Irrigation water allocation
mλ-Measure fuzzy chance-constrained programming
Nonlinearity
production functions
quadratic programming
regression analysis
risk
System benefits
uncertainty
water allocation
watersheds
China
System benefits
Nonlinearity
mλ-Measure fuzzy chance-constrained programming
Interval crop water production function
Irrigation water allocation
ISSN:0022-1694, 1879-2707
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•An inexact nonlinear mλ-measure fuzzy chance-constrained programming model is developed.•The model is applied to Heihe River Basin in northwest China for irrigation water management.•Interval quadratic crop water production functions are obtained based on interval regression method.•The model improves on the conventional fuzzy chance-constrained programming by introducing preference parameters.•The results can be examined by giving different confidence levels and preference parameters. An inexact nonlinear mλ-measure fuzzy chance-constrained programming (INMFCCP) model is developed for irrigation water allocation under uncertainty. Techniques of inexact quadratic programming (IQP), mλ-measure, and fuzzy chance-constrained programming (FCCP) are integrated into a general optimization framework. The INMFCCP model can deal with not only nonlinearities in the objective function, but also uncertainties presented as discrete intervals in the objective function, variables and left-hand side constraints and fuzziness in the right-hand side constraints. Moreover, this model improves upon the conventional fuzzy chance-constrained programming by introducing a linear combination of possibility measure and necessity measure with varying preference parameters. To demonstrate its applicability, the model is then applied to a case study in the middle reaches of Heihe River Basin, northwest China. An interval regression analysis method is used to obtain interval crop water production functions in the whole growth period under uncertainty. Therefore, more flexible solutions can be generated for optimal irrigation water allocation. The variation of results can be examined by giving different confidence levels and preference parameters. Besides, it can reflect interrelationships among system benefits, preference parameters, confidence levels and the corresponding risk levels. Comparison between interval crop water production functions and deterministic ones based on the developed INMFCCP model indicates that the former is capable of reflecting more complexities and uncertainties in practical application. These results can provide more reliable scientific basis for supporting irrigation water management in arid areas.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0022-1694
1879-2707
DOI:10.1016/j.jhydrol.2017.11.011