HDTO-DeepAR: A novel hybrid approach to forecast surface water quality indicators

Water is a vital resource supporting a broad spectrum of ecosystems and human activities. The quality of river water has declined in recent years due to the discharge of hazardous materials and toxins. Deep learning and machine learning have gained significant attention for analysing time-series dat...

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Bibliographic Details
Published in:Journal of environmental management 2024-02, Vol.352, p.120091-120091, Article 120091
Main Authors: Singh, Rosysmita Bikram, Patra, Kanhu Charan, Pradhan, Biswajeet, Samantra, Avinash
Format: Article
Language:English
Subjects:
comparative study
data collection
Deep learning
environmental management
HDTO-DeepAR
humans
river water
surface water
time series analysis
Time series forecasting
Water quality
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Summary:Water is a vital resource supporting a broad spectrum of ecosystems and human activities. The quality of river water has declined in recent years due to the discharge of hazardous materials and toxins. Deep learning and machine learning have gained significant attention for analysing time-series data. However, these methods often suffer from high complexity and significant forecasting errors, primarily due to non-linear datasets and hyperparameter settings. To address these challenges, we have developed an innovative HDTO-DeepAR approach for predicting water quality indicators. This proposed approach is compared with standalone algorithms, including DeepAR, BiLSTM, GRU and XGBoost, using performance metrics such as MAE, MSE, MAPE, and NSE. The NSE of the hybrid approach ranges between 0.8 to 0.96. Given the value’s proximity to 1, the model appears to be efficient. The PICP values (ranging from 95% to 98%) indicate that the model is highly reliable in forecasting water quality indicators. Experimental results reveal a close resemblance between the model’s predictions and actual values, providing valuable insights for predicting future trends. The comparative study shows that the suggested model surpasses all existing, well-known models. [Display omitted] •Presents the forecasting of surface water quality indicators using a novel HDTO-DeepAR method.•Describes the advantages of the suggested method over standalone approaches.•Highlights the significance of an optimization technique for accurate forecasting.
ISSN:0301-4797
1095-8630
DOI:10.1016/j.jenvman.2024.120091