Streamflow forecasting by SVM with quantum behaved particle swarm optimization

Ch, Sudheer; Anand, Nitin; Panigrahi, B. K.; Mathur, Shashi

doi:10.1016/j.neucom.2012.07.017

Cited by 135 publications

(50 citation statements)

References 19 publications

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“…Recently, SVM have been successfully extended to apply in regression and prediction applications [11,25,26]. SVM has been applied in the time-series prediction of river flow by Samsudin, Saad [6]; in SF prediction under multiple time scales by Asefa, Kemblowski [27]; in the real-time forecasting of flood stage by Yu, Chen [25]; in flood forecasting by [28]; in long-term discharge prediction by Lin, Cheng [29]; in the long-range forecast of SF by [30]; and in the monthly forecasting of SF by Guo, Zhou [31], Noori, Karbassi [32], Shabri and Suhartono [33], and Ch, Anand [34].…”

Section: Model Descriptionmentioning

confidence: 99%

Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines

2017

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Abstract. Stream flow (SF) prediction is considered as a very complex due to the hydrological systems of surface water are complex and dynamic. The reliable prediction of stream flow (SF) can be performed by either conceptual or data-driven based models. In the modelling of hydrological processes, the support vector machine (SVM) is a novel, data-driven approach. Hence, six SVM-based models were generated in this study to predict real time hourly SF in the Selangor River Basin from the water level and rainfall of upstream stations. These models composed of six different combinations of input variables and were trained and tested under hourly records of SF, rainfall, and water level over one year (2011). Among the SVM-based models, SVM-M6, which has nine input variables, was the most effective. Under the training and testing data sets, its correlation coefficient and mean absolute error values were 0.992, 0.953, 0.061 and 0.253 respectively.

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Section: Model Descriptionmentioning

confidence: 99%

Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines

2017

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“…Here, after several iterations, the system will be completely in a state of chaos. Vector layer can be expressed as: 11 (1 ) …”

Section: The Chaos Mechanismmentioning

confidence: 99%

“…Quantum particle swarm algorithm embodies two characteristics of quantum science: superposition and probability expression characteristics [11] . The two can make the particles exhibit more status, which increases the diversity of the population as a whole.…”

Section: The Quantum Particle Swarm With Chaoticmentioning

confidence: 99%

A Improved Algorithm of Quantum Particle Swarm with Fast Convergence

Zhang¹,

Li²,

Li³

2015

IJHIT

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An improved algorithm of quantum particle swarm with chaos is presented to solve the problem that the traditional particle swarm algorithm is easy to fall into local optimum and converges very slowly. Through analysis the current state of particles in the iteration, to determine and deal with the particle of poor performance, and keep the normal state continue to complete the search optimal solution, which effectively inhibit premature phenomenon of the particles, and improve the overall search ability of particle swarm. At the same time, in order to improve the performance of the algorithm, introducing chaos mechanism, further enhance the search ability of particle. The experiments of benchmark function show that the improved algorithm has obvious advantages compared with the other two algorithms, it has higher stability and accuracy and faster convergence speed at the same time.

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“…The QPSO algorithm has been greatly improved in the aspects of search ability, convergence speed, OPEN ACCESS solution accuracy and solving robustness, and it can also overcome the shortcoming that the standard PSO (SPSO) cannot guarantee the global convergence [1,2]. For this reason, QPSO has been widely used in bio-medicine, antenna design, combinatorial optimization, signal processing, neural networks and other fields [3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Quantum-Behaved Particle Swarm Optimization Based on a Novel Computing Way of Local Attractor

2015

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Quantum-behaved particle swarm optimization (QPSO), a global optimization method, is a combination of particle swarm optimization (PSO) and quantum mechanics. It has a great performance in the aspects of search ability, convergence speed, solution accuracy and solving robustness. However, the traditional QPSO still cannot guarantee the finding of global optimum with probability 1 when the number of iterations is limited. A novel way of computing the local attractor for QPSO is proposed to improve QPSO's performance in global searching, and this novel QPSO is denoted as EQPSO during which we can guarantee the particles are diversiform at the early stage of iterations, and have a good performance in local searching ability at the later stage of iteration. We also discuss this way of computing the local attractor in mathematics. The results of test functions are compared between EQPSO and other optimization techniques (including six different PSO and seven different optimization algorithms), and the results found by the EQPSO are better than other considered methods.

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Streamflow forecasting by SVM with quantum behaved particle swarm optimization

Cited by 135 publications

References 19 publications

Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines

Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines

A Improved Algorithm of Quantum Particle Swarm with Fast Convergence

An Enhanced Quantum-Behaved Particle Swarm Optimization Based on a Novel Computing Way of Local Attractor

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