Decision support for irrigation project planning using a genetic algorithm

Kuo, Sheng-Feng; Merkley, Gary P.; Liu, Chen‐Wuing

doi:10.1016/s0378-3774(00)00081-0

Cited by 89 publications

(49 citation statements)

References 11 publications

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“…The UH could be used as a mathematical description of a linear system [14][15][16][17][18][19]. In recent years, there are several applications of hydrological modelling that have been conducted [20][21][22]. The present study aims to carry out Gamma I method to analyse return period of flood hydrograph.…”

Section: Methodsmentioning

confidence: 99%

The analysis of Ancol polder system as flood prevention infrastructure in Jakarta

Prastica

2018

MATEC Web Conf.

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Abstract. Flooding in Jakarta is a common phenomenon that is caused by climate changes. One of the government's actions is building polders in Jakarta to prevent the flooding. However, these polders seem to not significantly affect it. This research aims to analyse Ancol Polder in North Jakarta as flood prevention infrastructure. This research uses Muskingum Cunge and O'Donnel and Muskingum Extended methods to analyse the hydrographs. This research analyses the performance of channel capacity by HEC-RAS modelling approach and observes its stabilization using GeoStudio 2012. The existing condition of the channel still accommodates the maximum outflow that enters the stream. Furthermore, its stability is safe in all of the reviewed STA in Pademangan Timur channel.

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Section: Methodsmentioning

confidence: 99%

The analysis of Ancol polder system as flood prevention infrastructure in Jakarta

Prastica

2018

MATEC Web Conf.

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“…Reca, et al presented an economic optimization model for water allocation in deficit irrigation situation (Reca et al, 2001). Some of researches have used a GA. Kuo, et al with the help of GA, developed an optimum model for determining optimum cropping pattern in an irrigation plan (Kuo et al, 2000). Wardlaw and Bhaktikul did the optimization of water resources utilization in cyclic irrigation systems with GA.…”

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.

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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...

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“…the height and speed of the UAV, the best starting-position for the next crop field, and the pressure of the spray system); (ii) investigation of different methodology for the fine-tuning control rules of UAV (e.g. Differential Evolution [16], Genetic Algorithms [17], [18], Hill-Climbing [19], NSGA-II [20]); (iii) an analysis of the feasibility of embedding the optimization methodology in UAV, leading to an autonomous architecture; (iv) an investigation of the methodologies required for planning route-aware of weather conditions. ACKNOWLEDGMENT…”

Section: Conclusion and Suggestions For Future Workmentioning

confidence: 99%

Fine-Tuning of UAV Control Rules for Spraying Pesticides on Crop Fields

Faiçal

Pessin

Filho

et al. 2014

2014 IEEE 26th International Conference on Tools With Artificial Intelligence

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Abstract-The use of pesticides in agriculture is essential to maintain the quality of large-scale production. The spraying of these products by using aircraft speeds up the process and prevents compacting of the soil. However, adverse weather conditions (e.g. the speed and direction of the wind) can impair the effectiveness of the spraying of pesticides in a target crop field. Thus, there is a risk that the pesticide can drift to neighboring crop fields. It is believed that a large amount of all the pesticide used in the world drifts outside of the target crop field and only a small amount is effective in controlling pests. However, with increased precision in the spraying, it is possible to reduce the amount of pesticide used and improve the quality of agricultural products as well as mitigate the risk of environmental damage. With this objective, this paper proposes a methodology based on Particle Swarm Optimization (PSO) for the fine-tuning of control rules during the spraying of pesticides in crop fields. This methodology can be employed with speed and efficiency and achieve good results by taking account of the weather conditions reported by a Wireless Sensor Network (WSN). In this scenario, the UAV becomes a mobile node of the WSN that is able to make personalized decisions for each crop field. The experiments that were carried out show that the optimization methodology proposed is able to reduce the drift of pesticides by fine-tuning of control rules.

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Decision support for irrigation project planning using a genetic algorithm

Cited by 89 publications

References 11 publications

The analysis of Ancol polder system as flood prevention infrastructure in Jakarta

The analysis of Ancol polder system as flood prevention infrastructure in Jakarta

Optimum Allocation of Water to the Cultivation Farms Using Genetic Algorithm

Fine-Tuning of UAV Control Rules for Spraying Pesticides on Crop Fields

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