Field-Scale Improvement of Water Allocation for Maize Cultivation Using Grey Wolf Optimization Algorithm

Behdarvandi, Hossein; Khoshnavaz, Saeb; Kharazi, Hossein Ghorbanizadeh; Nasab, Saeed Boroomand

doi:10.1007/s40996-020-00571-x

Cited by 5 publications

(2 citation statements)

References 56 publications

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“…It requires fewer control parameters to be tuned and provides proper balance between exploration and exploitation [37][38][39]. Furthermore, the grey wolf optimization algorithm has been successfully applied to solving several real-world problems such as damage identification of skeletal structures [40], design of reinforced concrete cantilevers [41], optimization of reservoir systems [42], and simulation of soil water content [43]. The basic operations of the grey wolf optimization algorithm are repeated during each iteration (Num_iter) until reaching the maximum number of iterations (Maxnum_iter).…”

Section: Complexitymentioning

confidence: 99%

Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

et al. 2021

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Highway tunnels are one of the paramount infrastructure systems that affect the welfare of communities. They are vulnerable to higher limits of deterioration, yet there are limited available funds for maintenance and rehabilitation. This state of circumstances entails the development of a deterioration model to forecast the performance condition behavior of critical tunnel elements. Accordingly, this research paper proposes an integrated deterioration prediction model for five highway tunnel elements, namely, cast-in-place tunnel liners, concrete interior walls, concrete portal, concrete ceiling slab, and concrete slab on grade. The developed deterioration model is envisioned in two fundamental components, which are model calibration and model assessment. In the first component, an integrated model of Gaussian process regression and a grey wolf optimization algorithm (GWO-GPR) is introduced for deterioration behavior prediction of highway tunnel elements. In this regard, the grey wolf optimizer is exploited to improve the prediction accuracies of the Gaussian process through optimal estimation of its hyper parameters and to automatically interpret the significant deterioration factors. The second component involves three tiers of performance evaluation comparison, statistical significance comparisons, and consolidated ranking to assess the prediction accuracies of the developed GWO-GPR model. In this regard, the developed model is validated against six widely acknowledged machine learning models, which are back-propagation artificial neural network, Elman neural network, cascade forward neural network, generalized regression neural network, support vector machines, and regression tree. Results demonstrate that the developed GWO-GPR model significantly outperformed other deterioration prediction models in the five tunnel elements. In cast-in-place tunnel liners it accomplished a mean absolute percentage error, mean absolute error, root mean square percentage error, root relative squared error, and relative absolute error of 1.65%, 0.018, 0.21%, 0.018, and 0.147, respectively. In this context, it was inferred that the developed GWO-GPR model managed to reduce the prediction errors of the back-propagation artificial neural network, Elman neural network, and support vector machines by 84.71%, 76.91%, and 69.6%, respectively. It can be concluded that the developed deterioration model can assist transportation agencies in creating timely and cost-efficient maintenance schedules of highway tunnels.

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Section: Complexitymentioning

confidence: 99%

Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

et al. 2021

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“…For example, in the area of optimal planning, Abbas et al [3] introduced a quantile regression algorithm to predict future trends of hydrological variables such as precipitation; Naghdi et al [4] combined with the NSGA-II multi-objective optimization algorithm which the results are better than existing conditions. In terms of intelligent algorithms, Behdarvandi et al [5] used grey wolf optimization algorithm (GWOA) to propose an optimal strategy for the allocation of water resources to maize cultivation based on the soil moisture requirements for maize cultivation; Mohammadrezapour et al [6] compared cuckoo optimization algorithm (COA) with genetic algorithm (GA), the results show that the COA can provide more optimal results in relative yield of crops and can allocate less water; Shourian et al [7] established a PSO-NFP model by coupling particle swarm algorithm (PSO) and a network flow programming (NFP) and evaluated the performance of the model, and the results indicated that the PSO-NFP model is applicable to the optimization problem of basin water resources. With the rising of emerging technologies such as big data and cloud computing, the amount of water resources data is becoming increasingly abundant, which shows the characteristics of big data such as scale, diversity and high speed [8].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Analysis of Regional Water Resources Allocation Decision-Making

Chen

2022

J. Phys.: Conf. Ser.

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Regional water allocation is of great importance to regional coordinated development. Therefore, this paper constructs a data-driven model for regional water allocation analysis to address the existing problems of the imbalance between water supply and demand, irrational utilization of water resources and water scarcity. Firstly, a classification of regional water allocation case is constructed through cluster analysis to obtain similar regions with the same salient characteristics. Then, the regions in the same category are divided into regions to be predicted and other regions, and the similarity of water resources allocation between regions to be predicted and other regions is calculated. The Criteria Importance Through Intercriteria Correlation (CRITC) method is used to calculate the weighted values of each index and forecast water demands of regions to be predicted. Finally, an example analysis of water allocation of each city in Hubei province in 2020 was carried out, and the results indicated that when the water allocation of each city in Hubei province was divided into 4 categories, the water allocation characteristics of different cities are presented. The relative errors of the predicted water demand do not exceed 5%, which is highly accurate and can provide decision support for rational water allocation.

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Application of improved grey wolf model in collaborative trajectory optimization of unmanned aerial vehicle swarm

Chen,

Nie

et al. 2024

Sci Rep

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With the development of science and technology and economy, UAV is used more and more widely. However, the existing UAV trajectory planning methods have the limitations of high cost and low intelligence. In view of this, grey Wolf algorithm is being used to achieve collaborative trajectory optimization of UAV groups. However, it is found that the Grey Wolf optimization algorithm (GWO) has the problem of weak cooperation. In this study, based on the traditional GWO pheromone factor is introduced to improve it.. Aiming at the problem of unstable performance of swarm intelligence optimization algorithm under dynamic threat, deep reinforcement learning is used to optimize the model. An unmanned aerial vehicle swarm trajectory planning model was constructed based on the improved grey wolf algorithm. Through experimental analysis, the optimal fitness value of the improved grey wolf algorithm was lower than 0.43 of the grey wolf algorithm. Compared with other algorithms, the fitness value of this algorithm is significantly reduced and the stability is higher. In complex scenarios, the improved grey wolf algorithm had a trajectory length of 70.51 km and a planning time of 5.92 s, which was clearly superior to other algorithms. The path length planned by the research and design model was 58.476 km, which was significantly smaller than the other three models. The planning time was 5.33 s and the number of path extension points was 46. The indicator values of the Unmanned Aerial Vehicle swarm trajectory planning model designed by this research were all smaller than the other three models. By analyzing the results, the model can achieve low-cost trajectory optimization, providing more reasonable technical support for unmanned aerial vehicle mission execution.

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Field-Scale Improvement of Water Allocation for Maize Cultivation Using Grey Wolf Optimization Algorithm

Cited by 5 publications

References 56 publications

Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

Data-Driven Analysis of Regional Water Resources Allocation Decision-Making

Application of improved grey wolf model in collaborative trajectory optimization of unmanned aerial vehicle swarm

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