Projection pursuit evaluation model of a regional surface water environment based on an Ameliorative Moth-Flame Optimization algorithm

In order to improve the accuracy of regional water environment assessments, an improved projection pursuit water quality evaluation model (AMFO-PPE) was developed that was based on the Ameliorative Moth-Flame Optimization (AMFO) algorithm. This approach utilized the Moth-Flame Optimization, which added the dynamic inertia weight, and the Kent chaotic map search strategy. The model was used to evaluate 16 typical riverside irrigation districts in the Sanjiang Plain. The spatial differentiation law and possible causes of the surface water quality were explored. The stability and reliability of the AMFO-PPE model for the regional water quality assessments were evaluated. The results showed that the water quality of the study area was generally excellent, but the TP index content was high. Areas with water quality Grades III and IV were mainly concentrated in the central and western regions. Regions with water quality Grades I and II were mainly concentrated in the central and eastern regions. The river network density is the main influencing factor of the water quality spatial distribution characteristics. Regions with a large river network density tend to have a better water quality. The application and utilization rates of phosphorus fertilizer have an important impact on the TP index. The NIM, RAGA-PPE model, FA-PPE model, and AMFO-PPE model were compared. The sum of the Spearman correlation coefficients calculated by randomly extracting 10 irrigation district values 25 times was 24.07906. The NIM distinction degree was greater than 1.09875 of the AMFO-PPE model. However, the NIM of standard values dot pitch was obviously insufficient. The AMFO-PPE model has a better stability and reliability than the other models. Therefore, this model performed as expected and has a value for application in regional water environment assessments.