Sand media filters are a key component of micro-irrigation systems since they help preventing emitter clogging, which greatly affects the system performance. Dissolved oxygen is an irrigation water quality parameter related to organic matter loading. Low values of dissolved oxygen can cause crop root hypoxia and, therefore, agronomic problems. Thus, an accurate prediction of dissolved oxygen values could be of great interest, especially if effluents are used in micro-irrigation systems. The aim of this study was to obtain a predictive model able to forecast the dissolved oxygen values at the outlets of sand media filters. In this study, a Gaussian process regression (GPR) model was used for predicting the output dissolved oxygen (DOo) from data corresponding to 547 filtration cycles of different sand filters using reclaimed effluent. This optimisation technique involves kernel parameter setting in the GPR training procedure, which significantly influences the regression accuracy. To this end, the height of the filter bed, filtration velocity and filter inlet values of the electrical conductivity, dissolved oxygen, pH, turbidity and water temperature were monitored and analysed. The significance of each variable on filtration performance is presented and a model for forecasting the outlet dissolved oxygen obtained. Regression with optimal hyperparameters was performed and a coefficient of determination of 0.90 for DOo was obtained when this new predictive GPR–based model was applied to the experimental dataset. Agreement between experimental data and the model confirmed the good performance of the latter.

中文翻译：

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使用高斯过程回归预测微灌砂介质过滤器出口溶解氧

砂介质过滤器是微灌系统的关键组件，因为它们有助于防止发射器堵塞，从而极大地影响系统性能。溶解氧是与有机物负荷相关的灌溉水质参数。低溶解氧值会导致作物根部缺氧，从而导致农艺问题。因此，准确预测溶解氧值可能会引起人们极大的兴趣，尤其是在微灌系统中使用废水时。本研究的目的是获得一个预测模型，能够预测砂介质过滤器出口处的溶解氧值。在本研究中，高斯过程回归 (GPR) 模型用于根据与使用再生废水的不同砂滤器的 547 个过滤循环对应的数据预测输出溶解氧 (DOo)。该优化技术涉及 GPR 训练过程中的内核参数设置，这对回归精度有显着影响。为此，对滤床高度、过滤速度和过滤器入口值的电导率、溶解氧、pH、浊度和水温进行了监测和分析。介绍了每个变量对过滤性能的重要性，并获得了预测出口溶解氧的模型。当这种新的基于 GPR 的预测模型应用于实验数据集时，执行了具有最佳超参数的回归，并获得了 0.90 的 DOo 决定系数。实验数据与模型之间的一致性证实了后者的良好性能。