The application provides a
water quality comprehensive evaluation method based on
random forest optimization of
water quality indexes, comprising the following steps: determining a
river section and
water quality indexes, obtaining a measured
data set, and calculating a water quality index
data set; dividing the water quality index
data set into a
training set and a prediction set; constructing a training model based on the
training set; constructing a prediction model based on the prediction set, predicting the water quality index, and evaluating the performance of the training model; based on the training result and the
evaluation result, determining the optimized water quality indexes according to the contribution degree
ranking; calculating the water quality index data set based on the optimized water quality indexes; gradually reducing the number of optimized water quality indexes, and calculating the water quality index data set; evaluating the prediction results of different numbers of optimized water quality indexes one by one, and determining the optimal water quality indexes; and calculating the water quality index of the river by using the optimal water quality indexes, so as to realize the water quality comprehensive evaluation. The method takes into account the accuracy and economy of water quality evaluation, reduces the observation of non-key indexes as much as possible, and reduces the evaluation cost.