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This study using computer image processing and artificial neural network sensor technologies constructs a method of identifying ice slurry density based on the value of ice color image. The method is applied to the Jinan section of the Yellow River through the ice image acquisition, R/G color extraction, network learning and training, the final output target value of ice or water, and the actual image...
To tackle the problems of low modeling efficiencies involved in implementing runoff forecasting using conventional modeling technologies, a visual modeling tool is established by integrating a visual modeling editor with the artificial neural network (ANN) modular to support interactive and fast modeling of the complex and dynamic runoff process. The workflow of visual modeling includes the prediction...
A displacement model using the back propagation algorithm of artificial neural networks (BP-ANN) optimized with a genetic algorithm (GA) was presented on the example of an arch-type dam in China. The settlement displacement analysis for a single point located on the dam was performed. The analysis consists of three stages: principal component analysis (PCA), BP-ANN modelling, and deformation forecast...
The paper presents a genetic neural network model based on the features of genetic algorithm and artificial neural network. It was applied to predict passenger capacity of China. The forecast result shows that genetic neural network model has a smaller margin of error than BP neural network model. Genetic neural network is rather effective than BP neural network. Using genetic neural network to predict...
In this paper, a novel artificial neural network ensemble rainfall forecasting model is proposed for rainfall forecasting based on K-nearest neighbor nonparametric estimation of regression. In this model, original data set are partitioned into some different training subsets via Bagging technology. Then different ANN algorithms and different network architecture generate diverse individual neural...
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