Artificial Intelligence, specifically, Boost-base methods, has successfully revolutionized the area of stock price forecasting in recent years. This article integrates the idea of the hybrid stacking ensemble learning to forecast the trend of stocks. In the utilized Google stock price dataset, the market features "Open," "High", "Low," and "Volume" are utilized as input variables for a proposed hybrid stacking model in line with various Artificial Intelligence (AI) techniques. Unlike traditional stock price forecasting methods, this hybrid stacking approach incorporates multiple AI models, including XGBoost, CatBoost, and Light Gradient-Boosting Machine (LightGBM), with Lasso regression serving as the meta-learner. By employing the intrinsic pattern hidden in the real-world stock market and the inherent stacking mode of the proposed methodology, the hybrid algorithm in this paper endeavors to enhance the prediction capability and capture the real-world operation mode in financial market. By leveraging distributed stock price data inputs and the stacking methodology, the model aims to enhance forecasting accuracy and better reflect actual stock market conditions. The multistate algorithm outperformed on the widely-employed Google datasets with several typical prediction evaluation criteria, including some classical format. The benchmark experiments on various baseline models demonstrate the effectiveness and the advantages of the developed multi-stage algorithm and enjoy the significant improvement on the Google standard market competition.

The limited land is under unprecedented pressure from production, living and ecology. In order to evaluate the land pressure in the Yangtze River Delta region in 1995, 2000, 2005, 2010, 2015, and 2020 from the perspective of production, living, and ecology. This study builds a land pressure evaluation index system based on a fuzzy comprehensive evaluation model using multi-source and multi-scale data. In order to investigate trade-offs and synergies among production, living, and ecology pressures, we use the mechanical equilibrium model in physics. We then analyze land pressure model reliability and uncertainty using Monte Carlo simulations. The results show that (1) Our model can effectively reveal the level of land pressure and reflect the land pressure geographical pattern of “high in the east and low in the west, high in the south and low in the north” that characterizes the Yangtze River Delta region. (2) While living and ecology pressures are tending to rise, production pressures are tending to decrease. (3) Except for Shanghai, the trade-off areas are primarily concentrated in economically successful regions with high production and living pressure and low ecology pressure. The coordinated areas are primarily found in northern Jiangsu Province and northern Anhui Province.