Variables’ classification via equivalence relations for the trophic state of a Mediterranean ecosystem

Abstract

The trophic state of an aquatic body is influenced by many biotic and abiotic factors. When lots of parameters affect a phenomenon, such as eutrophication, it is difficult to distinguish which are the ones that affect the ecosystem the most. In this paper, we propose an alternative way for data analysis, in order to avoid complex systems with many variables. For the examined Mediterranean shallow lake, the studied parameters are water temperature (°C), ammonia (NH4‐N) (mg/L), dissolved oxygen (mg/L), turbidity (NTU), pH, conductivity (mS/cm), and chlorophyll‐a (μg/L). We formed groups with the variables above based on fuzzy equivalence relations and from each group we chose the parameter influence the studied phenomenon the most. Numerical results of fuzzy linear regression showed strong agreement with the proposed method above and pH, NH4‐N, and dissolved oxygen are the variables influence this ecosystem more than the others. Practitioner points When having many parameters in a studied ecosystem, we propose a way we can distinguish the most representative ones, the parameters that influence more the phenomenon we study each time. Formation of groups in variables can be applied to many case studies in order to have a clear idea of our data and the relevance of each of them in our dependent one. Fuzzy linear regression can be used in order to check the final results and ensure that the equivalence relations are a such a good method used in the data analysis while the researchers save time in long procedures of analyzing parameters not very close involved to the phenomenon investigated every time.

Prediction of biogas production in anaerobic digestion of a full‐scale wastewater treatment plant using ensembled machine learning models

“…Spearman and Pearson correlation analyses were used to identify linear and nonlinear relationships between features and to eliminate redundant ones. This was crucial because having a high number of input features compared to the sample size can reduce statistical power and increase the risk of overfitting (Ellina et al, 2021;Nasief et al, 2019;Xu et al, 2022). Overfitting means that a model can perform well with the training dataset but the performance decreases with the test dataset, indicating that the model is not generalizable (Sun et al, 2023).…”