An interpretable hierarchical neural network insight for long-term water quality forecast: A study in marine ranches of Eastern China

Li, Dashe; Zhang, Xuan; Yang, Yufang; Yang, Hongjuan; Li, Shue

doi:10.1016/j.ecolind.2022.109771

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…In recent years, explainable artificial intelligence (XAI) has garnered significant attention for its ability to provide explanations for model results and enhance model credibility (Gunning et al., 2019; L. Li, Qiao, et al., 2022; D. Li et al., 2023). Hence, this research incorporates signal analysis methods, deep learning techniques, and XAI for simulating runoff.…”

Section: Discussionmentioning

confidence: 99%

Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model

Wu,

Wang,

Dong

et al. 2023

Water Resources Research

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Accurate runoff forecasting plays a vital role in issuing timely flood warnings. Whereas, previous research has primarily focused on historical runoff and precipitation variability while disregarding other factors’ influence. Additionally, the prediction process of most machine learning models is opaque, resulting in low interpretability of model predictions. Hence, this study develops an ensemble deep learning model to forecast runoff from three hydrological stations. Initially, time‐varying filtered based empirical mode decomposition (TVFEMD) is employed to decompose the runoff series into several internal mode functions (IMFs). Subsequently, the complexity of each IMF component is evaluated by the multi‐scale permutation entropy (MPE), and the IMFs are classified into high‐ and low‐frequency portions based on entropy values. Considering the high‐frequency portions still exhibit great volatility, robust local mean decomposition (RLMD) is adopted to perform secondary decomposition of the high‐frequency portions. Then, the meteorological variables processed by the Relief algorithm and variance inflation factor (VIF) features are employed as inputs, the individual subsequences of secondary and preliminary decomposition as outputs to the bidirectional gated recurrent unit (BiGRU) and extreme learning machine (ELM) models. Random forests (RF) are introduced to nonlinear ensemble the individual predicted sub‐models to obtain the final prediction results. The proposed model outperforms other models in various evaluation metrics. Meanwhile, due to the opaque nature of machine learning models, shapley is employed to assess the contribution of each selected meteorological variable to the long‐term trend of runoff. The proposed model could serve as an essential reference for precise flood prediction and timely warning.

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Section: Discussionmentioning

confidence: 99%

Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model

Wu,

Wang,

Dong

et al. 2023

Water Resources Research

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“…Mei presented a hybrid model based on CNN, GRU and attention mechanisms, in which different neuron weights can be adjusted by the attention layer to achieve accurate prediction of water quality [19]. Li proposed a dissolved oxygen prediction model combining stack structure, multi-attention mechanism and TCN, which can effectively improve the prediction accuracy of water quality parameters in Marine pastures and bring positive influence to the development of Marine fisheries [20]. Duan achieved effective prediction of tool wear status using a hybrid attention based on parallel deep learning [21].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Model to Predict Dissolved Oxygen for Efficient Water Quality in Intensive Aquaculture

Liu

Hassan

et al. 2023

IEEE Access

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Dissolved oxygen content is a key indicator of water quality in aquaculture environment. Because of its nonlinearity, dynamics, and complexity, which makes traditional methods face challenges in the accuracy and speed of dissolved oxygen content prediction. As a solution to these issues, this study introduces a hybrid model consisting of the Light Gradient Boosting Machine (LightGBM) and the Bidirectional Simple Recurrent Unit (BiSRU). Firstly, Linear interpolation and smoothing were used to identify significant parameters. LightGBM algorithm then determines the significance of dissolved oxygen by eliminating irrelevant variables and predicting dissolved oxygen in intensive aquaculture. Finally, the attention method was implemented to map the weighting and learning parameter matrices, so enabling the BiSRU's hidden states to be assigned different weights. The findings shown that the presented prediction model can accurately anticipate the fluctuating trend of dissolved oxygen over a 10-day period in just 122 seconds, and the accuracy rate reached 96.28%. Comparing the model effects of LightGBM -BiSRU, LightGBM -GRU, LightGBM-LSTM, and BiSRU -Attention takes the least time. Its higher prediction accuracy can provide an essential reference for intensive aquaculture water quality regulation.

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Lite approaches for long-range multi-step water quality prediction

Islam,

Newton,

Trevathan

et al. 2024

Stoch Environ Res Risk Assess

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Forecasting accurate water quality is very important in aquaculture, environment monitoring, and many other applications. Many internal and external factors influence water quality. Therefore, water quality parameters exhibit complex time series characteristics. Consequently, long-range accurate prediction of water quality parameters suffers from poor propagation of information from past timepoints to further future timepoints. Moreover, to synchronise the prediction model with the changes in the time series characteristics, periodic retraining of the prediction model is required and such retraining is to be done on resource-restricted computation devices. In this work, we present a low-cost training approach to improve long-range multi-step water quality prediction. We train a short-range predictor to save training effort. Then, we strive to achieve and/or improve long-range prediction using multi-step iterative ensembling during inference. Experimental results on 9 water quality datasets demonstrate that the proposed method achieves significantly lower error than the existing state-of-the-art approaches. Our approach significantly outperforms the existing approaches in several standard metrics, even in the case of future timepoints at long distances.

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An interpretable hierarchical neural network insight for long-term water quality forecast: A study in marine ranches of Eastern China

Cited by 5 publications

References 25 publications

Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model

Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model

A Novel Hybrid Model to Predict Dissolved Oxygen for Efficient Water Quality in Intensive Aquaculture

Lite approaches for long-range multi-step water quality prediction

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