Intelligent classification of land use based on BP neural network and stormwater simulation
The accuracy of land use data significantly impacts the simulation study of stormwater risks.Complex nonlinear relationships exist among different land features in land use classification.In order to enhance the accuracy of land classification data,this research introduces the Backpropagation(BP)neural network model with nonlinear mapping capabilities.A remote sensing image land use classification method based on deep learning is proposed.The study utilizes GF-2 remote sensing image data from the Yesanpo Scenic Area to conduct multiscale segmentation on the image.Spectral data reflecting land use information,along with DEM data and slope data,are chosen as input layer neurons,while land use types serve as output layer neurons.After normalization,an iterative training process is conducted to construct a BP neural network-based land use classification model.The model achieves an overall classification accuracy of 91％,with a Kappa coefficient of 0.8906.Based on the results obtained from this model,hydrological modeling and ArcGIS spatial analysis tools are employed to simulate and analyze the rain-induced flood-affected areas resulting from a once-in-a-century extreme precipitation event in the Yesanpo Scenic Area.Relevant strategies to mitigate rainfall-induced flooding are also proposed.
BP neural networkland use classificationmachine learningstormwater riskyesanpo scenic area
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维普
