基于人工神经网络的河湖蓝藻水华状态评价研究

面对依旧严峻的水环境问题,水资源的高效管理成为解决该问题的关键途径。其中,水华状态评价成为重中之重,本文针对河湖蓝藻水华状态评价过程中存在的非线性和不精确性问题,构建了基于人工神经网络的蓝藻水华状态评价模型,实现了固定站点监测和遥感监测信息的有效融合。并将该模型用于太湖蓝藻水华状态评价,研究表明:评价结果与实际情况相符,验证了模型的有效性和可行性,为蓝藻水华问题深入研究提供了思路。

Evaluation on the algae bloom of rivers and lakes based on artificial neural network

Faced with the serious water environment problem, the efficient management of water resources is the key way to solve this problem, and the state assessment of blooms has become a top priority. Aiming at the non-linearity and imprecision problems in the evaluation of algae blooms in rivers and lakes, an artificial neural network based algae bloom state evaluation model was constructed, and the effective integration of fixed site monitoring and remote sensing information was realized. The model was applied to the evaluation of algae blooms in Taihu Lake. The evaluation results were in accordance with the actual situation, which verified the validity and feasibility of the model, and provided the idea for the further study of algae bloom.