Quantification of river total phosphorus using integrative artificial intelligence models

• Published in: Ecological indicators 2023-09, Vol.153, p.110437, Article 110437
• Main Authors: Kim, Sungwon, Seo, Youngmin, Malik, Anurag, Kim, Seunghyun, Heddam, Salim, Yaseen, Zaher Mundher, Kisi, Ozgur, Singh, Vijay P.
• Format: Article
• Language: English
• Subjects: Discrete-wavelet algorithm; Double-platform synthetic technique; Stochastic gradient boosting; Support vector machines; Total phosphorus
• ISSN: 1470-160X; 1872-7034
• DOI: 10.1016/j.ecolind.2023.110437

•River total phosphorus is predicted using singular and double-platform synthetic.•The modeling platform is included data-preprocessing and predictive models.•Case study of two stations (Hwangji and Toilchun), South Korea, are inspected.•Stochastic gradient boosting model revealed the superior prediction results.•The proposed predictive model is indicated a robust intelligence model. Total phosphorus (T-P) refers to the concentration of phosphorus in water and is one of the important parameters for eutrophication in lakes and rivers. In current research, neuroscience dependent (i.e., singular and double-platform synthetic) approaches were employed to predict the river T-P concentration. Singular techniques were developed utilizing machine learning based (support vector machines (SVMs), stochastic gradient boosting (SGB)), and deep learning based multilayer perceptron (DMLP) models. Besides, double-platform synthetic techniques were developed by integrating a prior data-processing (Discrete-wavelet) algorithm with the singular techniques. Six input scenarios conditional on different water quantity and quality parameters acquired from two stations (Hwangji and Toilchun), South Korea, were employed for appraising singular and double-platform synthetic techniques. The various promoted models were evaluated using four statistical standards viz., mean absolute error (MAE), root mean square error (RMSE), scatter index (SI), and Pearson correlation coefficient (CCp), and four visual references viz., scatter diagram, box-and-whisker plot, violin plot, and Taylor diagram. It can be indicated from the outcomes that the double-platform synthetic techniques did not always lead to more accurate predictions than the singular techniques. Further, results also supplied that the SGB with the 6th input (MAE = 0.012 mg/L, RMSE = 0.014 mg/L, and CCp = 0.650 for Hwangji; MAE = 0.011 mg/L, RMSE = 0.017 mg/L, and CCp = 0.963 for Toilchun) scenario model demonstrated the best accuracy for predicting river T-P concentration by the singular techniques for both stations, whereas the discrete-wavelet SVMs with the 4th input (MAE = 0.007 mg/L, RMSE = 0.011 mg/L, and CCp = 0.765 for Hwangji) and discrete-wavelet SGB with the 5th input (MAE = 0.012 mg/L, RMSE = 0.017 mg/L, and CCp = 0.953 for Toilchun) scenario models provided the best predictive accuracy among double-platform synthetic techniques, respectively.