Application of genetic algorithms for irrigation water scheduling

Wardlaw, Robin; Bhaktikul, Kampanad

doi:10.1002/ird.121

Cited by 54 publications

(31 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this article, 0-1 approach is proposed by Wardlaw and Bhaktikul (2004a) is used for modelling the canal scheduling problem. In this approach, 0 and 1 imply operation state of canal as 'OFF' and 'ON' respectively in the time period.…”

Section: Particle Swarm Optimisationmentioning

confidence: 99%

“…With the division of the total irrigation period into a set of short time steps, say 1 day as one time step, the formulation of fitness function for canal scheduling model with the objective of optimising utilisation of water resources, while satisfying the soil moisture and supply canal capacity constraints was mathematically framed by Wardlaw and Bhaktikul (2004a) as given by the objective function in Equation (4) and constraints given by Equations (5) and (6).…”

Section: Model Structure For Canal Scheduling Problemmentioning

confidence: 99%

“…The decision regarding irrigation water supply was also monitored by a 48-h probability of rain forecast. Wardlaw and Bhaktikul (2004a) applied the GA approach to canal scheduling that combined canal delivery scheduling with in-field soil moisture requirement. This approach combines GA with deterministic soil moisture balance model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A binary particle swarm optimisation for generating optimal schedule of lateral canals

Mathur

Kumar²,

Pawde

2010

The IES Journal Part A: Civil & Structural Engineering

View full text Add to dashboard Cite

A binary particle swarm optimisation (BPSO) algorithm is presented for generating the optimal schedule of irrigation lateral canals. The approach has been applied to the Ghusali distributary on the right bank canal of Upper Wardha dam in India as a case study. The objective is to supply the optimum quantity of water to the crop in the field so as to meet crop water requirement as per guidelines of Food and Agriculture Organization (FAO) paper 56 subject to the canal capacity constraint. The results show that schedule prepared for lateral canals provided efficient utilisation of water resources and maintaining the equity in water distribution.

show abstract

Section: Particle Swarm Optimisationmentioning

confidence: 99%

Section: Model Structure For Canal Scheduling Problemmentioning

confidence: 99%

See 1 more Smart Citation

A binary particle swarm optimisation for generating optimal schedule of lateral canals

Mathur

Kumar²,

Pawde

2010

The IES Journal Part A: Civil & Structural Engineering

View full text Add to dashboard Cite

show abstract

“…Wardlaw and Bhaktikul did the optimization of water resources utilization in cyclic irrigation systems with GA. Results of research show that a GA can optimize irrigation planning in severe water stress situations (Wardlaw and Bhaktikul, 2004). Zia and Anwar used GA for scheduling of irrigation water in water limitation situations.…”

Section: Previous Workmentioning

confidence: 99%

Optimum Allocation of Water to the Cultivation Farms Using Genetic Algorithm

Saeidian¹,

Mesgari²,

Ghodousi³

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:The water scarcity crises in the world and specifically in Iran, requires the proper management of this valuable resource. According to the official reports, around 90 percent of the water in Iran is used for agriculture. Therefore, the adequate management and usage of water in this section can help significantly to overcome the above crises. The most important aspect of agricultural water management is related to the irrigation planning, which is basically an allocation problem. The proper allocation of water to the farms is not a simple and trivial problem, because of the limited amount of available water, the effect of different parameters, nonlinear characteristics of the objective function, and the wideness of the solution space. Usually To solve such complex problems, a meta-heuristic method such as genetic algorithm could be a good candidate. In this paper, Genetic Algorithm (GA) is used for the allocation of different amount of water to a number of farms. In this model, the amount of water transferable using canals of level one, in one period of irrigation is specified. In addition, the amount of water required by each farm is calculated using crop type, stage of crop development, and other parameters. Using these, the water production function of each farm is determined. Then, using the water production function, farm areas, and the revenue and cost of each crop type, the objective function is calculated. This objective function is used by GA for the allocation of water to the farms. The objective function is defined such that the economical profit extracted from all farms is maximized. Moreover, the limitation related to the amount of available water is considered as a constraint. In general, the total amount of allocated water should be less than the finally available water (the water transferred trough the level one canals). Because of the intensive scarcity of water, the deficit irrigation method are considered. In this method, the planning is on the basis of the optimum and limited allocation of water, and not on the basis of the each crop water requirement. According to the available literature, in the condition of water scarcity, the implementation of deficit irrigation strategy results in higher economical income. The main difference of this research with others is the allocation of water to the farms. Whilst, most of similar researches concentrate on the allocation of water to different water consumption sections (such as agriculture, industry etc.), networks and crops. Using the GA for the optimization of the water allocation, proper solutions were generated that maximize the total economical income in the entire study area. In addition, although the search space was considerably wide, the results of the implementation showed an adequate convergence speed. The repeatability test of the algorithm also proved that the algorithm is reasonably stable. In general the usage of GA algorithm can be considered as an efficient and trustable method for such irrigation planning problems. By optim...

show abstract

“…Once the seasonal water allocations for the crops had been determined, then solutions were determined for the daily water needs of the crops. Wardlaw and Bhaktikul (2004) studied the effectiveness of employing the genetic algorithm (GA) in determining solutions to a water scheduling problem. GA was tested in its ability to maintain a desirable level of soil moisture content on several farm plots.…”

Section: Introductionmentioning

confidence: 99%

On the performance of new local search heuristics for annual crop planning: case study of the Vaalharts irrigation scheme

Chetty

Adediran

2014

Journal of Experimental & Theoretical Artificial Intelligen

View full text Add to dashboard Cite

This paper investigates the capabilities of three new local search (LS) metaheuristic algorithms in determining solutions to an annual crop planning (ACP) problem at an existing Irrigation Scheme. ACP is an optimisation problem in agricultural planning which involves determining resource allocation solutions amongst the various crops that are required to be grown at an irrigation scheme, within a year. The LS algorithms investigated are the best performance algorithm (BPA), the iterative best performance algorithm (IBPA) and the largest absolute difference algorithm (LADA). To determine the relative merits of the solutions found by these algorithms, their solutions have been compared against the solutions of two well-known LS metaheuristic algorithms and four population-based metaheuristic algorithms in the literature. The results show that BPA, IBPA and LADA were competitive in determining solutions for this particular optimisation problem.

show abstract

Application of genetic algorithms for irrigation water scheduling

Cited by 54 publications

References 10 publications

A binary particle swarm optimisation for generating optimal schedule of lateral canals

A binary particle swarm optimisation for generating optimal schedule of lateral canals

Optimum Allocation of Water to the Cultivation Farms Using Genetic Algorithm

On the performance of new local search heuristics for annual crop planning: case study of the Vaalharts irrigation scheme

Contact Info

Product

Resources

About