Study of biological quality of lake waters based on phycocyanin using tree‐based methodologies

• Published in: Ecohydrology 2024-10, Vol.17 (7), p.n/a
• Main Authors: Kheimi, Marwan, Almadani, Mohammad, Ramezani‐Charmahineh, Abdollah, Zounemat‐Kermani, Mohammad
• Format: Article
• Language: English
• Subjects: Agricultural ecosystems; Algae; Algal blooms; Biomonitoring; Chemical elements; Dissolved oxygen; Drinking water; Elements; Eutrophication; harmful algal bloom; Industrial applications; lake water quality; Lakes; Machine learning; maximum limiting values; Moisture content; Parameters; Photosynthesis; Phycocyanin; Predictions; Root-mean-square errors; Seasonal variations; Seasonality; tree‐based models; Turbidity; Water; Water content; Water pollution; Water quality; Water resources
• ISSN: 1936-0584; 1936-0592
• DOI: 10.1002/eco.2688

The provision of drinking water, agricultural, and industrial applications by reservoirs has made lake exploration and monitoring unavoidable. The features of the ecosystem, particularly physical and chemical elements, influence the evaluation of the quality of water resources. Lakes undergo extensive qualitative changes due to their vast amount of water. In general, these bodies of water represent geological conditions as well as water contamination produced by natural and human activities. In the present research, the prediction of the amount of phycocyanin (fPC) in the water of Lake Michigan has been implemented employing four tree‐based machine learning techniques based on seasonality factors. Phycocyanin has significant effects on quality parameters such as turbidity, chlorophyll concentration, algal bloom, and dissolved oxygen in water by affecting the photosynthesis process of algae. Therefore, in this study, the prediction of the amount of phycocyanin dissolved in the lake water using the mentioned variables, along with the temperature of the water, specific conductance, and pH, has been able to interpret the quality of the water and the occurrence of phenomena such as algal blooms. The results of the models in predicting fPCs equal to 0.44 and 0.55 μg/L were consistent with the natural conditions of the lake, and it seems that ensemble tree–based models, along with the biological index of fPC, formed the right combination of input and output parameters in modeling and obtained the lowest prediction error (root‐mean‐square error [RMSE] boosted trees = 0.0140 and RMSE random forests = 0.0141 μg/L).