Optimizing Irrigation Water Allocation and Multicrop Planning Using Discrete PSO Algorithm

Noory, Hamideh; Liaghat, A; Parsinejad, Masoud; Bozorg‐Haddad, Omid

doi:10.1061/(asce)ir.1943-4774.0000426

Cited by 114 publications

(27 citation statements)

References 3 publications

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“…In recent years, evolutionary algorithms have also been utilized to optimize cropping patterns and irrigation scheduling (Noory et al, ). Nagesh Kumar et al .…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, evolutionary algorithms have also been utilized to optimize cropping patterns and irrigation scheduling (Noory et al, 2012). Nagesh Kumar et al (2006) presented a genetic algorithm (GA) model for calculating the optimal operating policy and the optimal crop water allocations from a single-purpose irrigation reservoir in India, and the results were compared with those from the linear programming (LP) method.…”

Section: Introductionmentioning

confidence: 99%

“…Georgiou and Papamichail (2008) optimized the cropping patterns by using simulated annealing algorithms (SAs). Noory et al (2012) presented linear and mixed-integer linear (MIL) models for optimizing an irrigation water allocation and multi-crop planning problem. The linear model was optimized with the LP method and continuous particle swarm optimization (CPSO) algorithm, and a detailed comparison of the results from the two methods was performed.…”

Section: Introductionmentioning

confidence: 99%

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Determination of optimized cropping patterns according to crop yield response under baseline condition and climate‐change condition

Abdi-Dehkordi

Bozorg‐Haddad

Chu

2018

Irrigation and Drainage

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Due to water shortage, climate change, and increased water demands, the optimal allocation of agricultural water is crucial, especially during a drought period. The objective of this study is to optimize the cropping patterns in the Aidoghmush River Basin in Iran for both the baseline condition and climate‐change condition. For this purpose, the genetic algorithm was applied and an optimization model was developed with the goal of maximizing the agricultural benefits for a 14‐year planning horizon. The baseline condition and climate‐change condition 14‐year periods employed in this study are 1987–2000 and 2026–2039, respectively. The results showed that the scenarios of cropping pattern changes with consideration of the crop yield response achieved higher benefits than those for the present condition and other optimal conditions without considering the crop yield response. With considering the optimum cropping patterns based on the crop yield response, the benefits in the planning horizon increased by 46.7 and 37.8% for the baseline condition and climate‐change condition, respectively, compared to the present condition. © 2018 John Wiley & Sons, Ltd.

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“…In recent years, evolutionary algorithms have also been utilized to optimize cropping patterns and irrigation scheduling (Noory et al, ). Nagesh Kumar et al .…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Determination of optimized cropping patterns according to crop yield response under baseline condition and climate‐change condition

Abdi-Dehkordi

Bozorg‐Haddad

Chu

2018

Irrigation and Drainage

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“…PSO algorithm is an evolutionary computational technique that was first developed by Eberhart and Kennedy (1995). It has been successfully used in several optimization studies to explore its efficiency (Chau 2004;Noory et al 2011;Sedghi et al 2013;Yuan et al 2008;Zhang et al 2009). PSO can be used to optimize the problems that are irregular, noisy, and changing over time and has been shown to compare favorably with GA in optimization problems (Liu et al 2005;Panda and Padhy 2008).…”

Section: Introductionmentioning

confidence: 99%

Improvement on the Existing Equations for Predicting Longitudinal Dispersion Coefficient

Alizadeh

Ahmadyar

Afghantoloee

2017

Water Resour Manage

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Accurate prediction of longitudinal dispersion coefficient (K) is a key element in studying of pollutant transport in rivers when the full cross sectional mixing has occurred. In this regard, several research studies have been carried out and different equations have been proposed. The predicted values of K obtained by different equations showed a great amount of uncertainty due to the complexity of the phenomenon. Therefore, there is still a need to make an improvement on the existing predictive models. In this study, a multi-objective particle swarm optimization (PSO) technique was used to derive new equations for predicting longitudinal dispersion coefficient in natural rivers. To do this, extensive field data, including hydraulic and geometrical characteristics of different rivers were applied. The results of this study were compared with those of the previous studies using the statistical error measures. The comparison revealed that the proposed model is superior to the previous ones. According to this study, PSO algorithm can be applied to improve the performance of the predictive equations by finding optimum values of the coefficients. The proposed model can be successfully applied to estimate the longitudinal dispersion coefficient for a wide range of rivers' characteristics.

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“…On the other hand, some heuristic algorithms, for example, genetic algorithm (GA) [19][20][21] and particle swarm optimization (PSO) algorithm [22,23], have also emerged in water demand-related forecasting. Without the constraint of continuity of functions, GA is characteristic of inner implicit parallelism and better global searching capability.…”

Section: Introduction Wmentioning

confidence: 99%

City Water Demand Forecasting Based on Improved BP Neural Network

Xing¹,

You²,

Zhang³

et al. 2017

JRST

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City water demand forecasting is of great significance in reducing the cost of electricity consumption and municipal planning. Back-propagation (BP) neural network has been widely adopted in water demand forecasting in recent years. But BP performs unsatisfactorily in terms of training time and global searching ability, so in this paper we improve BP by two heuristic algorithms, namely, genetic algorithm (ga) and particle swarm optimization (PSO), respectively. The testing and verification of the three algorithms (BP, ga+BP, PSo+BP) have been conducted on real-life water demand forecasting of Beijing city. The obtained results demonstrate that, in spite of the execution time consumed, both ga+BP and PSo+BP performed with higher accuracy and less errors than BP. The obtained results also illustrate that PSo+BP slightly outperformed ga+BP in terms of forecasting accuracy.

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Optimizing Irrigation Water Allocation and Multicrop Planning Using Discrete PSO Algorithm

Cited by 114 publications

References 3 publications

Determination of optimized cropping patterns according to crop yield response under baseline condition and climate‐change condition

Determination of optimized cropping patterns according to crop yield response under baseline condition and climate‐change condition

Improvement on the Existing Equations for Predicting Longitudinal Dispersion Coefficient

City Water Demand Forecasting Based on Improved BP Neural Network

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