Water quality simulation and aquatic plant configuration by integrating SVM and Harris Hawk algorithm

As urbanization accelerates, artificial intelligence-based environmental information modeling and decision support systems are playing an increasingly important role in water body ecological management. This study introduces support vector machines to construct water quality prediction models and integrates an improved Harris hawk optimization algorithm to enhance their robustness and parameter optimization efficiency. Based on this, an integrated optimization model combining support vector machines and the improved Harris hawk optimization algorithm is constructed for water quality simulation and aquatic plant configuration. Through collaborative optimization, this model achieves a feedback loop between nonlinear water quality modeling and plant scheduling strategies. The results show that the model has less error in the prediction of Chemical Oxygen Demand and the measured value, for example, the predicted value of the third sample point is 17.49mg/L, which is close to the measured value of 17.60mg/L, and the final evaluation of the water quality level shows higher consistency, and the misclassification rate decreases significantly. The study is of great significance to improve the scientific and accuracy of aquatic plant allocation in ecological management.