An Integrated Support Vector Machineand Quantum Behaved Particle Swarm Optimization Algorithm for Groundwater Level Forecasting
نویسنده
چکیده
Groundwater level prediction in a water basin plays a significant role in the management of groundwater resources. Aground water level forecasting system is developed in this study using Support vector Machines (SVM). Further Quantum behaved Particle Swarm Optimization (QPSO) function is employed in this study to determine the SVM parameters. Later, the proposed SVM-QPSO model is used in determining the groundwater level of Visakhapatnam region of Andhra Pradesh in India. The performance of the SVM-QPSO model is then compared with the ANN (Artificial Neural Networks).The results indicate that SVM-QPSO is a far better technique for predicting groundwater levels as it provides a high degree of accuracy and reliability.
منابع مشابه
Hybridization of Chaotic Quantum Particle Swarm Optimization with SVR in Electric Demand Forecasting
Abstract: In existing forecasting research papers support vector regression with chaotic mapping function and evolutionary algorithms have shown their advantages in terms of forecasting accuracy improvement. However, for classical particle swarm optimization (PSO) algorithms, trapping in local optima results in an earlier standstill of the particles and lost activities, thus, its core drawback ...
متن کاملStreamflow forecasting by SVM with quantum behaved particle swarm optimization
Accurate forecasting of streamflows has been one of the most important issues as it plays a key role in allotment of water resources. However, the information of streamflow presents a challenging situation; the streamflow forecasting involves a rather complex nonlinear data pattern. In the recent years, the support vector machine has been used widely to solve nonlinear regression and time serie...
متن کاملOPTIMUM SHAPE DESIGN OF DOUBLE-LAYER GRIDS BY QUANTUM BEHAVED PARTICLE SWARM OPTIMIZATION AND NEURAL NETWORKS
In this paper, a methodology is presented for optimum shape design of double-layer grids subject to gravity and earthquake loadings. The design variables are the number of divisions in two directions, the height between two layers and the cross-sectional areas of the structural elements. The objective function is the weight of the structure and the design constraints are some limitations on str...
متن کاملCancer Feature Selection and Classification Using a Binary Quantum-Behaved Particle Swarm Optimization and Support Vector Machine
This paper focuses on the feature gene selection for cancer classification, which employs an optimization algorithm to select a subset of the genes. We propose a binary quantum-behaved particle swarm optimization (BQPSO) for cancer feature gene selection, coupling support vector machine (SVM) for cancer classification. First, the proposed BQPSO algorithm is described, which is a discretized ver...
متن کاملAn Improved QPSO Algorithm for Parameters Optimization of LS-SVM
Aiming at the parameter optimization of least square support vector machine (LS-SVM), an improved quantum-behaved particle swarm optimization (IQPSO) algorithm for LS-SVM parameter selection was proposed. Based on QPSO, the algorithm optimizes particle initializing positions and improves solving speed and precision by sampling and linearizing methods. IQPSO LSSVM model was test by test function...
متن کامل