A watershed water quality prediction model based on attention mechanism and Bi-LSTM

Zhang, Qiang; Wang, Ruiqi; Qi, Ying; Wen, Fei

doi:10.1007/s11356-022-21115-y

Cited by 30 publications

(9 citation statements)

References 32 publications

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“…But because the CNN model is regional in nature, caution must be exercised when applying it to other geographic areas. [11] introduce the newly developed AT-BILSTM model designed for forecasting watershed water quality. For better accuracy in predictions, the use of a combination of Bi-LSTM and temporal attention is recommended.…”

Section: Literature Surveymentioning

confidence: 99%

An IoT Based Water Quality Classification Framework for Aqua-Ponds through Water and Environmental Variables using CGTFN Model

AREPALLI,

Amgoth

2024

Preprint

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Maintaining water quality in aquatic habitats is critical for the health of aquatic species, particularly for fish. This study pioneers an innovative method to water quality classification, leveraging IoT-driven data acquisition and meticulous data labelling with the Aqua-Enviro Index (AEI) by considering the fish habitats. Existing mechanisms fail to capture complex temporal dynamics and depend largely on large amounts of labelled data, exposing fundamental limits. In response, we describe the Deep learning based Convolutional Gated Recurrent Unit Tempo Fusion Network (CGTFN) model, which represents a considerable development in the evaluation of water quality. The model addresses these restrictions by seamlessly merging Convolutional Neural Networks (CNNs) for spatial pattern recognition and Gated Recurrent Units (GRUs) for temporal interactions. The Tempo Fusion mechanism combines spatial, temporal, and contextual data harmoniously, allowing for more sophisticated classifications by recognizing subtle interdependencies among environmental elements. The pioneering CGTFN model outperforms previous models, achieving 99.71% and 99.81% accuracy on both public-env and real-time-env datasets, respectively, exceeding established models at 98.2%. These remarkable findings highlight CGTFN's disruptive potential in water quality evaluation, bridging the gap between technology and environmental management, with ramifications ranging from aquaculture to resource sustainability.

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Section: Literature Surveymentioning

confidence: 99%

An IoT Based Water Quality Classification Framework for Aqua-Ponds through Water and Environmental Variables using CGTFN Model

AREPALLI,

Amgoth

2024

Preprint

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“…The model necessitates numerous parameters, and acquiring their values through direct observation is a challenging task [53]. Utilizing the LSTM approach driven by data, it is possible to effectively extract the spatio-temporal features of stand growth and their associations with climate, geography, and anthropogenic factors, based on historical data [54,55]. The process of training deep learning models typically necessitates a substantial amount of data.…”

Section: Estimation Accuracy and Interpretability Of The Hybrid Modelmentioning

confidence: 99%

3PG-MT-LSTM: A Hybrid Model under Biomass Compatibility Constraints for the Prediction of Long-Term Forest Growth to Support Sustainable Management

Qin

Zhu

et al. 2023

Forests

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Climate change is posing new challenges to forestry management practices. Thinning reduces competitive pressure in the forest by repeatedly reducing the tree density of forest stands, thereby increasing the productivity of plantations. Considering the impact of thinning on vegetation and physiological and ecological traits, for this study, we used Norway spruce (Picea abies) data from three sites in the PROFOUND dataset to parameterize the 3-PG model in stages. The calibrated 3-PG model was used to simulate the stand diameter at breast height and the stem, root, and leaf biomass data on a monthly scale. The 3PG-MT-LSTM model uses 3-PG simulation data as the input variable. The model uses a long short-term memory neural network (LSTM) as a shared layer and introduces multi-task learning (MTL). Based on the compatibility rules, the interpretability of the model was further improved. The models were trained using single-site and multi-site data, respectively, and multiple indicators were used to evaluate the model accuracy and generalization ability. Our preliminary results show that, compared with the process model and LSTM algorithm without MTL and compatibility rules, the hybrid model has higher biomass simulation accuracy and shows a more realistic biomass response to environmental driving factors. To illustrate the potential applicability of the model, we applied light (10%), moderate (20%), and heavy thinning (30%) at intervals of 10, 15, 20, 25, 30 years. Then, we used three climate scenarios—SSP1-2.6, SSP2-4.5, and SSP5-8.5—to simulate the growth of Norway spruce. The hybrid model can effectively capture the impact of climate change and artificial management on stand growth. In terms of climate, temperature and solar radiation are the most important factors affecting forest growth, and under warm conditions, the positive significance of forest management is more obvious. In terms of forest management practices, less frequent light-to-moderate thinning can contribute more to the increase in forest carbon sink potential; high-intensity thinning can support large-diameter timber production. In summary, moderate thinning should be carried out every 10 years in the young-aged forest stage. It is also advisable to perform light thinning procedures after the forest has progressed into a middle-aged forest stage. This allows for a better trade-off of the growth relationship between stand yield and diameter at breast height (DBH). The physical constraint-based hybrid modeling approach is a practical and effective tool. It can be used to measure long-term dynamic changes in forest production and then guide management activities such as thinning to achieve sustainable forest management.

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“…The multivariate time series build predictive models by analyzing historical time series data and correlations between individual factors [48]. For multi-element water quality time series data, different element features have different effects on water quality prediction.…”

Section: Water Quality Correlation Analysismentioning

confidence: 99%

Water Quality Prediction Based on LSTM and Attention Mechanism: A Case Study of the Burnett River, Australia

Chen

Yang

et al. 2022

Sustainability

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Prediction of water quality is a critical aspect of water pollution control and prevention. The trend of water quality can be predicted using historical data collected from water quality monitoring and management of water environment. The present study aims to develop a long short-term memory (LSTM) network and its attention-based (AT-LSTM) model to achieve the prediction of water quality in the Burnett River of Australia. The models developed in this study introduced an attention mechanism after feature extraction of water quality data in the section of Burnett River considering the effect of the sequences on the prediction results at different moments to enhance the influence of key features on the prediction results. This study provides one-step-ahead forecasting and multistep forward forecasting of dissolved oxygen (DO) of the Burnett River utilizing LSTM and AT-LSTM models and the comparison of the results. The research outcomes demonstrated that the inclusion of the attention mechanism improves the prediction performance of the LSTM model. Therefore, the AT-LSTM-based water quality forecasting model, developed in this study, demonstrated its stronger capability than the LSTM model for informing the Water Quality Improvement Plan of Queensland, Australia, to accurately predict water quality in the Burnett River.

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A watershed water quality prediction model based on attention mechanism and Bi-LSTM

Cited by 30 publications

References 32 publications

An IoT Based Water Quality Classification Framework for Aqua-Ponds through Water and Environmental Variables using CGTFN Model

An IoT Based Water Quality Classification Framework for Aqua-Ponds through Water and Environmental Variables using CGTFN Model

3PG-MT-LSTM: A Hybrid Model under Biomass Compatibility Constraints for the Prediction of Long-Term Forest Growth to Support Sustainable Management

Water Quality Prediction Based on LSTM and Attention Mechanism: A Case Study of the Burnett River, Australia

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