Abstract
In the past few decades, with the country’s rapid development, water pollution has become a significant problem many countries face. Most of the existing water quality evaluation uses a single time-invariant model to simulate the evolution process, which cannot directly describe the complex behavior of long-term water quality evolution. In addition, the traditional comprehensive index method, fuzzy comprehensive evaluation, and gray pattern recognition have more subjective factors. It can lead to an inevitable subjectivity of the results and weak applicability. Given these shortcomings, this paper proposes a deep learning-improved comprehensive pollution index method to predict future water quality development. As a first processing step, the historical data is normalized. Three deep learning models, multilayer perceptron (MLP), recurrent neural network (RNN), and long short-term memory (LSTM), are used to train historical data. The optimal data prediction model is selected through simulation and comparative analysis of relevant measured data, and the improved entropy weight comprehensive pollution index method is applied to evaluate future water quality changes. Compared with the traditional time-invariant evaluation model, the feature of this model is that it can effectively reflect the development of water quality in the future. Moreover, the entropy weight method is introduced to balance the errors caused by subjective weight. The result shows that LSTM performs well in accurately identifying and predicting water quality. And the deep learning–improved comprehensive pollution index method can provide helpful information and enlightenment for water quality change, which can help improve the water quality prediction and scientific management of coastal water resources.
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References
Almalawi A, Khan A I, Alqurashi F, et al (2022) Modeling of remora optimization with deep learning enabled heavy metal sorption efficiency prediction onto biochar[J]. Chemosphere 303:135065
Almalawi A, Alsolami F et al (2022b) An IoT based system for magnify air pollution monitoring and prognosis using hybrid artificial intelligence technique. Environ Res Sect A(206-): 206
Chen P (2021) Effects of the entropy weight on TOPSIS[J]. Expert Systems with Applications 168:114186
Eddine B I, Guastaldi E, Zirulia A, et al (2021) Trend analysis and spatiotemporal prediction of precipitation, temperature, and evapotranspiration values using the ARIMA models: case of the Algerian Highlands 2021[J]. Arab J Geosci pp. 13:24
Fan YJ, Xu KK et al (2020) Spatiotemporal modeling for nonlinear distributed thermal processes based on KL decomposition, MLP and LSTM network. IEEE ACCESS 8:25111–25121
Gao Y, Qian H, Ren W, et al (2020) Hydrogeochemical characterization and quality assessment of groundwater based on integrated-weight water quality index in a concentrated urban area[J]. J Cleaner Prod 260:121006
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Jia X, Klemeš J J, Alwi S R W, et al (2020) Regional water resources assessment using water scarcity pinch analysis[J]. Resour Conserv Recycl 157:104749
Jiang Y, Li C et al (2021) Data-driven method based on deep learning algorithm for detecting fat, oil, and grease (FOG) of sewer networks in urban commercial areas. Water Res 207:117797
Kamarudin M H, Ismail Z H, Saidi N B (2021) Deep learning sensor fusion in plant water stress assessment: A comprehensive review[J]. Appl Sci 11(4):1403
Ke S, Chen J, Zheng X (2021) Influence of the subsurface physical barrier on nitrate contamination and seawater intrusion in an unconfined aquifer[J]. Environ Pollut 284:117528.
Khan A I, Alsolami F, Alqurashi F, et al (2022) Novel energy management scheme in IoT enabled smart irrigation system using optimized intelligence methods[J]. Eng Appl Artif Intell 114:104996
Khullar S, Singh N (2022) Water quality assessment of a river using deep learning Bi-LSTM methodology: forecasting and validation. Environ Sci Pollut Res 29(9):12875–12889
Kumar R, Singh S et al (2021) Revealing the benefits of entropy weights method for multi-objective optimization in machining operations: a critical review. J Mater Res Technol-JMR&T 10:1471–1492
Li Q, Yang Y, Yang L, et al (2023) Comparative analysis of water quality prediction performance based on LSTM in the Haihe River Basin, China[J]. Environ Sci Pollut Res 30(3):7498–7509
Mou LC, Ghamisi P et al (2018) “Deep recurrent neural networks for hyperspectral image classification” (vol 55, pg 3639, 2017). IEEE Trans Geosci Remote Sens 56(2):1214–1215
Sayah M, Guebli D et al (2021) Robustness testing framework for RUL prediction deep LSTM networks. ISA Trans 113:28–38
Sherstinsky A (2020) Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network[J]. Physica D: Nonlinear Phenomena 404:132306
Wang J, Zhang L, Zhang W, et al (2019) Reliable model of reservoir water quality prediction based on improved ARIMA method[J]. Environ Eng Sci 36(9):1041–1048
Xia JJ, Zeng J (2022) Environmental factors assisted the evaluation of entropy water quality indices with efficient machine learning technique. Water Resour Manage 36(6):2045–2060
Yin Z, Luo Q, Wu J, et al (2021) Identification of the long-term variations of groundwater and their governing factors based on hydrochemical and isotopic data in a river basin[J]. J Hydrol 592:125604
Zeng Q, Luo X et al (2022) The pollution scale weighting model in water quality evaluation based on the improved fuzzy variable theory. Ecol Indic 135:108562
Zhang Y, Li C, Jiang Y, et al (2022) Accurate prediction of water quality in urban drainage network with integrated EMD-LSTM model[J]. J Clean Prod 354:131724
Zheng T, Zheng X, Chang Q, et al (2021) Timescale and effectiveness of residual saltwater desalinization behind subsurface dams in an unconfined aquifer[J]. Water Resour Res 57(2): e2020WR028493
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All of the parts of this research were conducted by Professor Jia; Haitao Yang conducted the research. Haitao Yang and Fan Yang performed statistical analysis; Haitao Yang wrote the paper; Xiao Yang, Ruchun Wei, and Xiao Yang revised this paper. All authors have read and approved the final manuscript.
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Yang, H., Jia, C., Yang, F. et al. Water quality assessment of deep learning-improved comprehensive pollution index: a case study of Dagu River, Jiaozhou Bay, China. Environ Sci Pollut Res 30, 66853–66866 (2023). https://doi.org/10.1007/s11356-023-27174-z
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DOI: https://doi.org/10.1007/s11356-023-27174-z