Retraction Note to: Machine learning-based prediction of urban soil environment and corpus translation teaching

Abstract

In recent years, due to the development of science and technology, the existence of objective factors such as people’s abuse of pesticides and unreasonable development, the soil has been seriously polluted, and its quality has also declined. People’s living environment and the agricultural products they need are also affected by it. Frequent problems have seriously affected the sustainable development of the ecological environment. This article takes the central and western parts of a province as the survey object, uses the methods of geological accumulation index and comprehensive pollution index to analyze the soil pollution status of the survey area, and uses the index soil fertility index to evaluate the soil fertility in the fresh food production area. The AERMOD model was used to calculate the dry sedimentation, wet sedimentation, and total sedimentation of organic waste gas. In addition, in the era of big data, with the emergence and development of artificial intelligence technology, translation technology has developed rapidly and has become the new favorite of the translation industry, which has had an important impact on the translation industry. It also puts forward new requirements for the construction of the corpus. Constructing the ecology of soil environment prediction from the perspective of ecological translation is helpful to construct the connotation of interconnection, mutual restriction and interaction among system components. This thesis is guided by the theoretical thoughts of ecological translation, and tries to explore the advancement of soil environment prediction by constructing an ecological view of translation. Through the application of project management concepts in daily teaching, it can help a student to better master the process and methods of translation and improve their translation ability.