Climate change in river basin based on machine learning and regional financial risk identification

Abstract

Using risk to consider the validity of the verification results, applying machine learning to the tax authorities to prevent financial risk identification, can realize the two wheel drive of artificial experience and big data analysis and open up a new vision path for improving the financial risk identification management. In this paper, by randomly selecting the forest list and taking the business as an example, we establish an identity model for the risk of forging other tax invoices. Through verification and reduction, we can know that the model is consistent and reliable, has the accuracy of prediction, and can be used as a reference for tax authorities. This paper can use a large number of machine learning applications to detect climate change in the river basin. It is an important part of ecological security and sustainable development in environmental change to quantitatively distinguish climate change and the impact of human activities on watershed water process. In this paper, based on the development and spatial distribution pattern of the watershed, the change characteristics of aquatic and climatic conditions in different ecological periods were analyzed by estimating the trend and adverse methods. Correlation analysis and principal component analysis were used to clarify the characteristics and driving mechanism of climate change in the land use/cover river basin. On the basis of the above research, this paper analyzes the land and land use and analyzes the impacts of climate change and human regional development activities in different ecological regions on hydrological processes.