Research on Water Level Adjustment Based on Markov Analysis Method and Differential Multivariate Regression Model

This paper presents a comprehensive study on water level adjustment utilizing a periodic Markov chain analysis algorithm and a differential multivariate regression model. The periodic Markov chain method is employed to analyze transitions in water usage patterns, where input-output ratios and transition matrices are leveraged to infer the impact of water usage proportions and volumes on water levels. Furthermore, a differential multivariate regression model algorithm is developed to quantify the influence of various factors on water level variations across different time periods. An inverse difference control estimation is also constructed to enhance the precision of the model. The integration of these methodologies provides a robust framework for dynamic water level management and adjustment.