Eco-environmental landscape characterization data based on artificial intelligence and neural network algorithms

In this study, the ecological environment landscape pattern index was selected to construct the ecological environment landscape data representation model under the self-organized feature mapping network (SOM) technique. The input data to the model were monitored under unsupervised conditions and made more sensitive to specific characterization information in the neuronal structure (Hebb), which resulted in different groups of regular data. The moving window method shows that the landscape index is unstable under the 1000-3000m window and the magnitude of change begins to decrease at the 4000m scale. The data tends to stabilize at 5000m scale, and the stability of the data decreases at 6000-7000m, and the anomalous data increases at this time. In terms of landscape level, the aggregation and connectivity of the overall landscape of the study area increased and landscape fragmentation, complexity and diversity decreased under the 4000m window. The land use change model based on SOM network can well reflect the law of land use change in the sample area, which greatly expands the spatial analysis research method of land use change.