Using deep learning to protect the diversity of the ecological environment Based on the prevention and control of alien species

Abstract

Abstract Ecological environmental protection has gradually changed from the original protected species to the protection of the entire ecosystem, controlling the destruction of biological resources and the ecological environment. The protection of the diversity of the ecological environment is one of the important measures to prevent the destruction of the ecological environment, and the invasion of alien species is one of the important reasons for the destruction of the ecological diversity. The expansion of invasive alien species not only brings various disturbances or damages to the native ecosystem, but also pays a great price for human health and economic and social development. This article uses deep learning related models and methods to identify alien species to help strengthen the prevention and control of alien species. Based on the data of 3000 reporters of the Asian Hornet invading the United States, this paper trains and uses the Bi-LSTM model and the CNN model to identify the pictures of the Asian Hornet reporter’s explanation and feedback, and makes full use of the above two models through XGBoost The output identification results and the temperature, humidity, air pressure and other parameters of the corresponding area were finally successfully screened out of the invalid reports of sightings of the Asian Hornet reported by the masses. Compared with the 1,386 test data, the accuracy rate was as high as 0.956. The results can effectively identify and Prevent and control new species and protect ecological diversity.