Chlorophyll-a (hereafter referred to as Chl-a) is a recognized indicator for phytoplankton abundance and biomass –hence, an effective estimation of the trophic condition– of water bodies as lakes, reservoirs and oceans. Indeed, Chl-a is the primary molecule responsible for photosynthesis. A strong and robust Bayesian nonparametric technique, termed Gaussian process regression (GPR) approach, for foretelling the dependent variable Chl-a concentration in Tanes reservoir from a dataset concerning to 268 samples is shown in this paper. Ten years (2006–2015) of monitoring water quality variables (biological and physico-chemical independent variables) in the Tanes reservoir were used to build this mathematical GPR-relied model. As an optimizer, the method known as Limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGSB) iterative algorithm was used; this allows the selection of kernel optimal parameters during the GPR training phase, which greatly determines the regression precision. The results of the current investigation can be summarized in two. Firstly, the relevance of each input variable on Chl-a concentration in Tanes reservoir is determined. Secondly, the Chl-a can be successfully predicted using this hybrid LBFGSB/GPR–relied model (R² and r values were 0.8597 and 0.9306, respectively). The concordance between observed data and the model clearly proves the high efficiency of this innovative approach.

叶绿素-a（以下称为叶绿素-a）是公认的浮游植物丰度和生物量的指标，因此是湖泊，水库和海洋等水体营养状况的有效估算。实际上，Chl-a是负责光合作用的主要分子。本文展示了一种强大而强大的贝叶斯非参数技术，称为高斯过程回归（GPR）方法，用于从268个样本的数据集中预测Tanes油藏中因变量Chl-a的浓度。Tanes水库中监测水质变量（生物学和物理化学独立变量）的十年（2006-2015年）被用来建立这个数学上基于GPR的模型。作为优化器，使用了称为受限内存的Broyden-Fletcher-Goldfarb-Shanno（LBFGSB）迭代算法的方法。这允许在GPR训练阶段选择内核最佳参数，这在很大程度上决定了回归精度。当前调查的结果可以归纳为两个。首先，确定每个输入变量与Tanes油藏中Chl-a浓度的相关性。其次，可以使用这种LBFGSB / GPR混合模型成功预测Chl-a（R ²和r值分别为0.8597和0.9306。观测数据与模型之间的一致性清楚地证明了这种创新方法的高效率。