Information Prediction of Groundwater Pollution Sources Based on Improved Random Forest Algorithm

The prediction of information regarding groundwater pollution sources is a crucial prerequisite for effective control and restoration of groundwater pollution. However, the inherent complexity and uncertainty within groundwater systems often result in substantial errors and limitations when employing traditional prediction methods. In response to these challenges, this research paper proposes a novel information prediction approach for groundwater pollution sources, leveraging an enhanced random forest algorithm. This method strategically combines the global and local search capabilities of an algorithm with a groundwater flow and migration model. The objective is to predict essential details such as the location, intensity, release time, and duration of groundwater pollution sources. To enhance the convergence speed of the algorithm and the quality of solutions, the paper introduces several improvements to the random forest algorithm. The enhancements encompass the introduction of an adaptive parameter adjustment mechanism, the incorporation of various neighborhood search strategies, and the adoption of a multi-objective optimization framework. Notably, the improved random forest algorithm utilizes Sobol sequences during initialization to diversify and homogenize the solution set. Simultaneously, a crisscross strategy is employed to bolster the local search capabilities, thereby enhancing solution accuracy and stability. Validation of the proposed method is conducted through both numerical experiments and real-world case analyses. The findings affirm the effectiveness and superiority of the enhanced random forest algorithm. Moreover, the research establishes the feasibility and practicality of this approach. The article, therefore, presents a novel concept and tool for predicting information related to groundwater pollution sources. It furnishes a scientific foundation and technical support crucial for the control and restoration of groundwater pollution.