Random forest-based understanding and predicting of the impacts of anthropogenic nutrient inputs on the water quality of a tropical lagoon

Abstract

Seawater quality degradation is caused by diverse, non-linearly interacting factors, knowledge of which is essential for understanding and predicting water quality trends. Currently, most water-quality research has been based on certain assumptions to employ linear approaches for solving simplified problems, such as numerical simulations or cumulative impact assessments. To improve the accuracy and ease of prediction, the random forest method has been increasingly employed as a good alternative to traditional prediction methods. In the present study, the random forest method was adopted to construct a model of the water quality response of Xincun Lagoon to anthropogenic nutrient inputs based on a limited amount of sample data, aiming to (a) identify the critical sources of nutrient inputs that affect the meeting of water quality objectives so as to minimize the socioeconomic impact on secondary stakeholders; and (b) predict the impact of a reduction of anthropogenic nutrient inputs on water quality improvement. It can be seen from the results that the intensity of stressors generated by different human activities presents an obvious non-linear superposition pattern, and the random forest method is one of the feasible solutions to this phenomenon; in addition, the impact on the lagoon ecosystem is not directly related to the intensity of the pressure source, for example, coastal aquaculture is more important than shallow sea cage aquaculture. Therefore, the method established in this paper can be used to identify the key pressure sources during the restoration of the lagoon environment, so as to achieve the unity of economy and effectiveness.