Irrigation Planning Under Uncertainty—A Multi Objective Fuzzy Linear Programming Approach

Abstract

The problem of irrigation planning becomes more complex by considering an uncertainty. The uncertainties can be tackled by formulating the problem of irrigation planning as Fuzzy Linear Programming (FLP). FLP models can incorporate the scenario of real world problem. In the present study, Multi Objective Fuzzy Linear Programming (MOFLP) irrigation planning model is formulated for deriving the optimal cropping pattern plan for the case study of Jayakwadi project in the Godavari river sub basin in Maharashtra State, India. Four conflicting objectives are considered such as Net Benefits (NB), Crop/Yield Production (CP), Employment Generation/Labour Requirement (EG) and Manure Utilization (MU). Four different cases are considered to incorporate the uncertainty in MOFLP model. To include the uncertainty in irrigation planning problem only objectives are taken as fuzzy and constraints are crisp in nature in Case-I. To consider the uncertainty involved in availability of resources, in Case-II the stipulations are fuzzy. The technological coefficients are fuzzy in Case-III. The Case-IV includes both technological coefficients and stipulations fuzzy. The level of satisfaction (λ) works out to be 0.58, 0.50, 0.50 and 0.28 respectively for Case-I to IV. The results obtained in Case-IV are more realistic and promising as it involves the uncertainty in technological coefficients and stipulations simultaneously.

A multiobjective fuzzy linear programming model for sustainable integrated operation of a multireservoir system

“…(), Zeng et al . (), Regulwar and Gurav (), Mirajkar and Patel (), Morankar et al . () and many other researchers.…”

An uncertainty-based framework for agricultural water-land resources allocation and risk evaluation

“…In recent years, a number of studies on agricultural irrigation resources or agricultural land-use resources allocation involving uncertainties have been reported (Cai et al, 2004;Regulwar and Gurav, 2011;Dai and Li, 2013;Dong et al, 2014;Li and Guo, 2015). Among them, inexact two-stage stochastic programming (ITSP) has proved to be an effective technique for dealing with uncertain coefficients with known probability distributions and independent uncertain coefficients (Maqsood and Huang, 2003;.…”

Optimal Operation of Multipurpose Reservoir for Irrigation Planning with Conjunctive Use of Surface and Groundwater

“…Therefore, it is not always certain that the results obtained from less detailed models are less truthful than the results from complex model. Regulwar and Gurav [30] [31] have worked on the optimal cropping pattern for sustainable irrigation by maximizing different conflicting objectives simultaneously, however, without taking in to consideration the availability of groundwater in the command of the reservoir and the socio economic aspect of growing certain crops based on minimum requirement of the region, while optimizing the cropping pattern. They tackled the uncertainty in the different parameters under the fuzzy environment.…”