Accurately predicting the concentration of organochlorine pesticides (OCPs) presents a challenge due to their complex sources and environmental behaviors. In this study, we introduced a novel and advanced model that combined the power of three distinct techniques: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), and a deep learning network of Long Short-Term Memory (LSTM). The objective is to characterize the variation in OCPs concentrations with high precision. Results show that the hybrid two-stage decomposition coupled models achieved an average symmetric mean absolute percentage error (SMAPE) of 23.24 % in the empirical analysis of typical surface water. It exhibited higher predictive power than the given individual benchmark models, which yielded an average SMAPE of 40.88 %, and single decomposition coupled models with an average SMAPE of 29.80 %. The proposed CEEMDAN-VMD-LSTM model, with an average SMAPE of 13.55 %, consistently outperformed the other models, yielding an average SMAPE of 33.53 %. A comparative analysis with shallow neural network methods demonstrated the advantages of the LSTM algorithm when coupled with secondary decomposition techniques for processing time series datasets. Furthermore, the interpretable analysis derived by the SHAP approach revealed that precipitation followed by the total phosphorus had strong effects on the predicted concentration of OCPs in the given water. The data presented herein shows the effectiveness of decomposition technique-based deep learning algorithms in capturing the dynamic characteristics of pollutants in surface water.

中文翻译：

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基于两阶段分解的可解释深度学习方法阐明和预测悬浮颗粒物中的有机氯农药


由于有机氯农药 （OCP） 的来源和环境行为复杂，准确预测有机氯农药 （OCP） 的浓度是一项挑战。在这项研究中，我们引入了一种新颖而先进的模型，它结合了三种不同技术的力量：具有自适应噪声的完全集成经验模态分解 （CEEMDAN）、变分模态分解 （VMD） 和长短期记忆 （LSTM） 的深度学习网络。目的是高精度地表征 OCPs 浓度的变化。结果表明，在典型地表水的经验分析中，混合两阶段分解耦合模型实现了 23.24% 的平均对称平均绝对百分比误差 （SMAPE）。它表现出比给定的单个基准模型更高的预测能力，后者的平均 SMAPE 为 40.88 %，单个分解耦合模型的平均 SMAPE 为 29.80 %。所提出的 CEEMDAN-VMD-LSTM 模型的平均 SMAPE 为 13.55%，始终优于其他模型，平均 SMAPE 为 33.53%。与浅层神经网络方法的比较分析表明，LSTM 算法在与二级分解技术相结合处理时间序列数据集时具有优势。此外，由 SHAP 方法得出的可解释分析表明，降水后跟总磷对给定水中 OCP 的预测浓度有很强的影响。本文提供的数据显示了基于分解技术的深度学习算法在捕获地表水中污染物动态特征方面的有效性。