GA-BP neural networks for environmental quality assessment
Liu Yijun, Tang Jiali, Jiang Hongfen, Zhu Guangping, Dan Chen, Yu Zhimin · 2010
High-quality environmental assessments with neural networks contribute to informed decision making, in support of sustainable development. In this study, the BP neural network improved by the genetic algorithm is applied to the problem of environmental quality assessment. GA is used to optimize the initial weights of the BP neural network to make full use of global optimization of GA and local accurate searching of the BP algorithm. Matlab Software and its neural network toolbox are used to simulate and compute. The experiment results show that the GA-BP neural network has a good performance for environmental quality assessment. Furthermore, compared with the conventional BP algorithm, the GA-BP learning algorithm has more rapid convergence and better assessment accuracy of environmental quality.