Intelligent predicting of salt pond’s ion concentration based on support vector regression and neural network

The constant dynamic changes in salt pond make it difficult to achieve accurate prediction of ion concentration. It is of great significance to get the accurate prediction of potassium ion concentration in salt pools for the actual production of potash fertilizer. In this paper, some machine learning methods, such as support vector regression (SVR), AdaBoost regressor, K neighbor regressor, gradient boosting regressor, extra trees regressor and neural network regressor, have been used to build the prediction models. In the experiment, the MSE and R2 of the K+ concentration by using SVR in test data set reach 0.26385 and 0.9414, which are better than other models. Therefore, the SVR model has high research value in the field of salt pool ion concentration prediction.