This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

With the rapid development of China’s economy and society, the reform of talent training mode for business administration has become the most concerned and valued issue in the current teaching work in colleges and universities. From the current situation of undergraduate education curriculum system construction in vocational colleges, the traditional teaching methods of higher English still occupy the majority. The all English bilingual course for the undergraduate major of business administration takes the basic knowledge of language and the theory of natural science as the core content. Therefore, this paper will focus on how to build a perfect talent training mode for business administration majors that meets the actual needs and employment direction of students, and put forward specific teaching strategies in order to provide more application-oriented and professional development platforms for business administration students.

The Human Development Index, which accounts for both net foreign income and the total value of goods and services generated domestically, illustrates how income becomes less significant as Gross National Income (GNI) rises by using the logarithm of income. South Africa ranks 109th out of 189 countries in the Human Development Index (HDI) within the Brazil, Russia, India, China and South Africa (BRICS) economic bloc, raising long-term sustainability concerns. The study explores the relationship between economic, demography, policy indicators and human development in South Africa. South Africa’s unique status as a developing country within the BRICS economic group, alongside its lengthy history of racial discrimination, calls for a sophisticated approach to understanding Human Development. Existing research considered economic, demography, policy indicators independently; the gap of understanding their interconnection and long-term effects in the South African contexts exists. The study addresses the gap by using Autoregressive-Distributed Lag (ARDL) approach to investigate the short-term and the long-term relationship between economic, demography, policy indicators and human development in South Africa. By discovering these links, the study hopes to provide useful insights for policymakers seeking to promote sustainable human development in South Africa. The findings indicate that growth in GDP is a key factor in the HDI since it shows that there are more financial resources available for human development. By discovering these links, the study hopes to provide useful insights for policymakers seeking to promote sustainable human development in South Africa.

English writing is an important output skill cultivated in college English courses. And in the age of informationization, college English writing teaching relies on the support of information technology. In order to cope with the solution of this dilemma, Production-Oriented Approach(POA) proposes Teacher-Student Collaborative Assessment (TSCA), which creates a new form of assessment. Through TSCA, teachers can not only alleviate the heavy teaching load, but also fully tap the network resources to efficiently use students' time before, during and after class to maximize and optimize the teaching effect. In this paper, the teaching concept of TSCA is used to conduct writing teaching experiments, to study and analyze the writing ability of students who write effectively, so as to provide insights for the practice of English writing teaching.