Unmanned aerial vehicle remote sensing and machine learning techniques to predict CODMn in Taizhou

To rapidly and accurately determine the spatial distribution of permanganate index (CODMn) concentrations in surface water, we employed multispectral unmanned aerial vehicle (UAV) remote sensing for water quality inversion monitoring. River extraction was conducted using the normalized difference water index (NDWI) in conjunction with a self-developed parameter a. Fifteen characteristic variables were derived through exponential calculation, and modeling was performed in Jiaobei River using both single-band variables and those with a correlation coefficient exceeding 0.3 and tested in Nanguan River. The model selection included the elastic net regression algorithm, random forest regression algorithm, and gradient boosting regression algorithm. The findings indicated that accurate river extraction is achieved when NDWI exceeds 0.2 and a remains below 0. The gradient boosting regression algorithm proved to be the superior method for constructing an inversion model of CODMn in the Jiaobei River. When applied to the Nanguan River, the model also yielded reliable inversion results. However, in the concentration range of 3 to 5mg/L, the inversion results were less accurate due to an insufficient sample size. A quantitative remote sensing monitoring model of water quality in Taizhou was established using a multispectral UAV, providing technical support for accurate water pollution control.