Forecast of chlorophyll-a concentration as an indicator of phytoplankton biomass in El Val reservoir by utilizing various machine learning techniques: A case study in Ebro river basin, Spain

Abstract

The trophic condition of bodies of water, such as oceans, lakes, and reservoirs, can be accurately assessed thanks to the use of chlorophyll-a, or Chl-a, as an indicator of phytoplankton biomass and abundance. In fact, the main molecule in charge of photosynthesis is Chl-a. This work presents a powerful and reliable nonparametric method for predicting the concentration of Chl-a in El Val reservoir using a dataset containing 240,765 samples: the Support Vector Regression (SVR) with different kinds of kernels. This mathematical SVR-relied model was constructed using five years (2018–2022) of water quality variable monitoring (physico-chemical independent variables) in the El Val reservoir (located in the northeast of Spain). For comparison, M5 model trees, a the Multilayer Perceptron (MLP), that is a particular type of artificial neural network (ANN), and multivariate linear regression (MLR) were also used for the same observed data. The Grid Search (GS) algorithm was employed as an optimizer; this approach greatly improves the regression precision by allowing the optimal kernel parameters to be chosen during the SVR training phase. There are two ways to sum up the findings of this investigation. First, it is determined how relevant each input variable is to the Chl-a concentration in the El Val reservoir. Second, this hybrid GS/SVR-relied model with PUK kernel can accurately predict the Chl-a (R2 and r values were 0.8989 and 0.9499, respectively). The model’s agreement with the observed data amply proves the remarkable efficacy of this creative strategy.