Abstract
Accurate water quality prediction plays a vital role in sustainable water management. The artificial intelligence models commonly used in water management including artificial neural networks, long short-term memory, support vector machine, adaptive neuro-fuzzy inference are reviewed. The hybrid models, artificial intelligence coupled with decomposition and optimization algorithms, are thoroughly discussed. After a brief introduction of each model, a review of recent publications to understand the potential and application of these models in surface water quality modeling is proposed. Based on the selected 60 papers published over the past decade (2013–2023), the statistical results in terms of data pre-processing, input and output parameters, modeling approaches, and performance evaluation are analyzed to reveal the latest trends. Artificial neural network has flexible variants to suit many scenarios and long short-term memory model has the advantage of processing time series data. Most remarkable performance in the single and hybrid algorithms is found. Hybrid algorithms generate more satisfactory results in predicting accuracy. The application of these models improves the decision-making mechanisms for environmental governance and show immense potential for various applications.
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References
Abba SI, Hadi SJ, Sammen SS, Salih SQ, Abdulkadir RA, Quoc Bao P, Yaseen ZM (2020) Evolutionary computational intelligence algorithm coupled with self-tuning predictive model for water quality index determination. J Hydrol 587. https://doi.org/10.1016/j.jhydrol.2020.124974
Alizamir M, Heddam S, Kim S, Mehr AD (2021) On the implementation of a novel data-intelligence model based on extreme learning machine optimized by bat algorithm for estimating daily chlorophyll-a concentration: Case studies of river and lake in USA. J Clean Prod 285:124868. https://doi.org/10.1016/j.jclepro.2020.124868
Alvi M, Batstone D, Mbamba CK, Keymer P, French T, Ward A, Dwyer J, Cardell-Oliver R (2023) Deep learning in wastewater treatment: a critical review. Water Res 245:120518–120518. https://doi.org/10.1016/j.watres.2023.120518
Antanasijevic D, Pocajt V, Peric-Grujic A, Ristic M (2020) Multilevel split of high-dimensional water quality data using artificial neural networks for the prediction of dissolved oxygen in the Danube River. Neural Comput Appl 32(8):3957–3966. https://doi.org/10.1007/s00521-019-04079-y
Antonopoulos VZ, Gianniou SK (2022) Analysis and modelling of temperature at the water – atmosphere interface of a lake by energy budget and ANNs models. Environ Process 9(1):15. https://doi.org/10.1007/s40710-022-00572-0
Ay M, Kisi O (2017) Estimation of dissolved oxygen by using neural networks and neuro fuzzy computing techniques. KSCE J Civ Eng 21(5):1631–1639. https://doi.org/10.1007/s12205-016-0728-6
Azad A, Karami H, Farzin S, Mousavi S-F (2019) Modeling river water quality parameters using modified adaptive neuro fuzzy inference system. Water Sci Eng 12(1):45–54. https://doi.org/10.1016/j.wse.2018.11.001
Barzegar R, Aalami MT, Adamowski J (2020) Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model. Stoch Env Res Risk A 34(2):415–433. https://doi.org/10.1007/s00477-020-01776-2
Barzegar R, Moghaddam AA, Adamowski J, Ozga-Zielinski B (2018) Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model. Stoch Env Res Risk A 32(3):799–813. https://doi.org/10.1007/s00477-017-1394-z
Burchard-Levine A, Liu S, Vince F, Li M, Ostfeld A (2014) A hybrid evolutionary data driven model for river water quality early warning. J Environ Manag 143:8–16. https://doi.org/10.1016/j.jenvman.2014.04.017
Chen H, Yang J, Fu X, Zheng Q, Song X, Fu Z, Wang J, Liang Y, Yin H, Liu Z, Jiang J, Wang H, Yang X (2022) Water quality prediction based on LSTM and attention mechanism: A case study of the Burnett River, Australia. Sustainability. https://doi.org/10.3390/su142013231
Chen Y, Song L, Liu Y, Yang L, Li D (2020) A review of the artificial neural network models for water quality prediction. Appl Sci. https://doi.org/10.3390/app10175776
Díaz Muñiz C, Alonso Fernández JR, García Nieto PJ, Alvarez Antón JC (2015) Modelling algal abnormal proliferation in a reservoir using support vector regression: a case study. Ecohydrology 8(6):1109–1118. https://doi.org/10.1002/eco.1568
Dong L, Zhang J (2021) Predicting polycyclic aromatic hydrocarbons in surface water by a multiscale feature extraction-based deep learning approach. Sci Total Environ 799:149509. https://doi.org/10.1016/j.scitotenv.2021.149509
Du N, Liang X, Wang C, Jia L (2022) Multi-station joint long-term water level prediction model of Hongze Lake based on RF-Informer. Int Conf Inf Sci Parallel Distrib Syst (ISPDS) 25–30. https://doi.org/10.1109/ISPDS56360.2022.9874178
Dragomiretskiy K, Zosso D (2014) Variational Mode Decomposition. IEEE Trans Signal Process 62(3):531–544. https://doi.org/10.1109/TSP.2013.2288675
Emamgholizadeh S, Kashi H, Marofpoor I, Zalaghi E (2014) Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models. Int J Environ Sci Technol 11(3):645–656. https://doi.org/10.1007/s13762-013-0378-x
Fijani E, Barzegar R, Deo R, Tziritis E, Skordas K (2019) Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters. Sci Total Environ 648:839–853. https://doi.org/10.1016/j.scitotenv.2018.08.221
García-Nieto PJ, García-Gonzalo E, Alonso Fernández JR, Díaz Muñiz C (2018) Predictive modelling of eutrophication in the Pozón de la Dolores lake (Northern Spain) by using an evolutionary support vector machines approach. J Math Biol 76(4):817–840. https://doi.org/10.1007/s00285-017-1161-2
Gharibi H, Mahvi AH, Nabizadeh R, Arabalibeik H, Yunesian M, Sowlat MH (2012) A novel approach in water quality assessment based on fuzzy logic. J Environ Manag 112:87–95. https://doi.org/10.1016/j.jenvman.2012.07.007
Haghiabi AH, Nasrolahi AH, Parsaie A (2018) Water quality prediction using machine learning methods. Water Qual Res J 53(1):3–13. https://doi.org/10.2166/wqrj.2018.025
Hameed M, Sharqi SS, Yaseen ZM, Afan HA, Hussain A, Elshafie A (2017) Application of artificial intelligence (AI) techniques in water quality index prediction: a case study in tropical region, Malaysia. Neural Comput Appl 28:S893–S905. https://doi.org/10.1007/s00521-016-2404-7
Hien Than N, Dinh Ly C, Van Tat P (2021) The performance of classification and forecasting Dong Nai River water quality for sustainable water resources management using neural network techniques. J Hydrol 596:126099. https://doi.org/10.1016/j.jhydrol.2021.126099
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: Theory and applications. Neurocomputing 70(1–3):489–501. https://doi.org/10.1016/j.neucom.2005.12.126
Huang J, Gao J (2017) An ensemble simulation approach for artificial neural network: An example from chlorophyll a simulation in Lake Poyang, China. Ecol Inform 37:52–58. https://doi.org/10.1016/j.ecoinf.2016.11.012
Huang NE, Shen Z, Long SR, Wu MLC, Shih HH, Zheng QN, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. P Roy Soc A-Math Phy 454(1971):903–995. https://doi.org/10.1098/rspa.1998.0193
Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685. https://doi.org/10.1109/21.256541
Ji X, Shang X, Dahlgren RA, Zhang M (2017) Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China. Environ Sci Pollut Res 24(19):16062–16076. https://doi.org/10.1007/s11356-017-9243-7
Jiao J, Zhao L, Huang S, Ma Q (2023) Water quality prediction based on IGRA-ISSA-LSTM model. Water Air Soil Pollut 234(3). https://doi.org/10.1007/s11270-023-06117-x
Jin T, Cai S, Jiang D, Liu J (2019) A data-driven model for real-time water quality prediction and early warning by an integration method. Environ Sci Pollut Res 26(29):30374–30385. https://doi.org/10.1007/s11356-019-06049-2
Kar AK (2016) Bio inspired computing – A review of algorithms and scope of applications. Expert Syst Appl 59:20–32. https://doi.org/10.1016/j.eswa.2016.04.018
Khadr M, Elshemy M (2017) Data-driven modeling for water quality prediction case study: The drains system associated with Manzala Lake, Egypt. Ain Shams Eng J 8(4):549–557. https://doi.org/10.1016/j.asej.2016.08.004
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. https://doi.org/10.1007/s11356-021-13875-w
Li J, Abdulmohsin HA, Hasan SS, Kaiming L, Al-Khateeb B, Ghareb MI, Mohammed MN (2019) Hybrid soft computing approach for determining water quality indicator: Euphrates River. Neural Comput Appl 31(3):827–837. https://doi.org/10.1007/s00521-017-3112-7
Li Q, Yang Y, Yang L, Wang Y (2023) Comparative analysis of water quality prediction performance based on LSTM in the Haihe River Basin, China. Environ Sci Pollut Res 30(3):7498–7509. https://doi.org/10.1007/s11356-022-22758-7
Liang S, Han S, Sun Z (2015) Parameter optimization method for the water quality dynamic model based on data-driven theory. Mar Pollut Bull 98(1):137–147. https://doi.org/10.1016/j.marpolbul.2015.07.004
Liang ZY, Zou R, Chen X, Ren TY, Su H, Liu Y (2020) Simulate the forecast capacity of a complicated water quality model using the long short-term memory approach. J Hydrol 581. https://doi.org/10.1016/j.jhydrol.2019.124432
Liu S, Tai H, Ding Q, Li D, Xu L, Wei Y (2013) A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction. Math Comput Model 58(3):458–465. https://doi.org/10.1016/j.mcm.2011.11.021
Loos S, Shin CM, Sumihar J, Kim K, Cho J, Weerts AH (2020) Ensemble data assimilation methods for improving river water quality forecasting accuracy. Water Res 171:115343. https://doi.org/10.1016/j.watres.2019.115343
Lu H, Ma X (2020) Hybrid decision tree-based machine learning models for short-term water quality prediction. Chemosphere 249:126169. https://doi.org/10.1016/j.chemosphere.2020.126169
Manembu P, Kewo A, Welang B (2015) Missing data solution of electricity consumption based on Lagrange Interpolation case study: IntelligEnSia data monitoring. Int Conf Electr Eng Inform (ICEEI) 511–516. https://doi.org/10.1109/ICEEI.2015.7352554
Najah A, El-Shafie A, Karim OA, El-Shafie AH (2014) Performance of ANFIS versus MLP-NN dissolved oxygen prediction models in water quality monitoring. Environ Sci Pollut Res 21(3):1658–1670. https://doi.org/10.1007/s11356-013-2048-4
Najah Ahmed A, Binti Othman F, Abdulmohsin Afan H, Khaleel Ibrahim R, Ming Fai C, Shabbir Hossain M, Ehteram M, Elshafie A (2019) Machine learning methods for better water quality prediction. J Hydrol 578:124084. https://doi.org/10.1016/j.jhydrol.2019.124084
Najwa Mohd Rizal N, Hayder G, Mnzool M, Elnaim BME, Mohammed AO, Khayyat MM (2022) Comparison between regression models, support vector machine (SVM), and artificial neural network (ANN) in river water quality prediction. Processes. https://doi.org/10.3390/pr10081652
Noori N, Kalin L, Isik S (2020) Water quality prediction using SWAT-ANN coupled approach. J Hydrol 590. https://doi.org/10.1016/j.jhydrol.2020.125220
Park Y, Cho KH, Park J, Cha SM, Kim JH (2015) Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea. Sci Total Environ 502:31–41. https://doi.org/10.1016/j.scitotenv.2014.09.005
Raheli B, Aalami MT, El-Shafie A, Ghorbani MA, Deo RC (2017) Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River. Environ Earth Sci 76(14):503. https://doi.org/10.1007/s12665-017-6842-z
Rajaee T, Khani S, Ravansalar M (2020) Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: A review. Chemometr Intell Lab 200:103978. https://doi.org/10.1016/j.chemolab.2020.103978
Sarkar A, Pandey P (2015) River water quality modelling using artificial neural network technique. Aquat Procedia 4:1070–1077. https://doi.org/10.1016/j.aqpro.2015.02.135
Shah MI, Javed MF, Alqahtani A, Aldrees A (2021) Environmental assessment based surface water quality prediction using hyper-parameter optimized machine learning models based on consistent big data. Process Saf Environ 151:324–340. https://doi.org/10.1016/j.psep.2021.05.026
Shen J, Qin Q, Wang Y, Sisson M (2019) A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading. Ecol Modell 398:44–54. https://doi.org/10.1016/j.ecolmodel.2019.02.005
Shi B, Wang P, Jiang J, Liu R (2018) Applying high-frequency surrogate measurements and a wavelet-ANN model to provide early warnings of rapid surface water quality anomalies. Sci Total Environ 610:1390–1399. https://doi.org/10.1016/j.scitotenv.2017.08.232
Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. Proc Congr Evol Comput CEC 1945–1950. https://doi.org/10.1109/CEC.1999.785511
Song C, Yao L (2022) Application of artificial intelligence based on synchrosqueezed wavelet transform and improved deep extreme learning machine in water quality prediction. Environ Sci Pollut Res 29(25):38066–38082. https://doi.org/10.1007/s11356-022-18757-3
Song C, Yao L, Hua C, Ni Q (2021a) A novel hybrid model for water quality prediction based on synchrosqueezed wavelet transform technique and improved long short-term memory. J Hydrol 603. https://doi.org/10.1016/j.jhydrol.2021.126879
Song C, Yao L, Hua C, Ni Q (2021b) A water quality prediction model based on variational mode decomposition and the least squares support vector machine optimized by the sparrow search algorithm (VMD-SSA-LSSVM) of the Yangtze River, China. Environ Monit Assess 193(6). https://doi.org/10.1007/s10661-021-09127-6
Tang X, Huang M (2021) Inversion of chlorophyll-a concentration in Donghu Lake based on machine learning algorithm. Water. https://doi.org/10.3390/w13091179
Tiyasha TTM, Yaseen ZM (2020) A survey on river water quality modelling using artificial intelligence models: 2000–2020. J Hydrol 585:124670. https://doi.org/10.1016/j.jhydrol.2020.124670
Torres ME, Colominas MA, Schlotthauer G, Flandrin P (2011) A complete ensemble empirical mode decomposition with adaptive noise. IEEE Int Conf Acoust Speech Signal Process (ICASSP) 4144–4147. https://doi.org/10.1109/ICASSP.2011.5947265
Tutz G, Ramzan S (2015) Improved methods for the imputation of missing data by nearest neighbor methods. Comput Stat Data Anal 90:84–99. https://doi.org/10.1016/j.csda.2015.04.009
Wan H, Xu R, Zhang M, Cai Y, Li J, Shen X (2022) A novel model for water quality prediction caused by non-point sources pollution based on deep learning and feature extraction methods. J Hydrol 612:128081. https://doi.org/10.1016/j.jhydrol.2022.128081
Wu H, Cheng S, Xin K, Ma N, Chen J, Tao L, Gao M (2022) Water quality prediction based on multi-task learning. Int J Environ Res Public Health 19(15). https://doi.org/10.3390/ijerph19159699
Wu J, Wang Z (2022) A hybrid model for water quality prediction based on an artificial neural network, wavelet transform, and long short-term memory. Water. https://doi.org/10.3390/w14040610
Wu K-P, Wang S-D (2009) Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space. Pattern Recogn 42(5):710–717. https://doi.org/10.1016/j.patcog.2008.08.030
Xia J, Zeng J (2022) Environmental factors assisted the evaluation of entropy water quality indices with efficient machine learning technique. Water Res Manag 36(6):2045–2060. https://doi.org/10.1007/s11269-022-03126-z
Xu L, Liu S (2013) Study of short-term water quality prediction model based on wavelet neural network. Math Comput Model 58(3–4):801–807. https://doi.org/10.1016/j.mcm.2012.12.023
Xu Y, Ma C, Liu Q, Xi B, Qian G, Zhang D, Huo S (2015) Method to predict key factors affecting lake eutrophication – A new approach based on Support Vector Regression model. Int Biodeter Biodegr 102:308–315. https://doi.org/10.1016/j.ibiod.2015.02.013
Xue J, Shen B (2020) A novel swarm intelligence optimization approach: sparrow search algorithm. Syst Sci Control Eng 8(1):22–34. https://doi.org/10.1080/21642583.2019.1708830
Yahya ASA, Ahmed AN, Othman FB, Ibrahim RK, Afan HA, El-Shafie A, Fai CM, Hossain MS, Ehteram M, Elshafie A (2019) Water quality prediction model based support vector machine model for ungauged river catchment under dual scenarios. Water 11(6). https://doi.org/10.3390/w11061231
Yang X-S (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Inspir Com 2(2):78–84. https://doi.org/10.1504/IJBIC.2010.032124
Yaseen ZM, Ramal MM, Diop L, Jaafar O, Demir V, Kisi O (2018) Hybrid adaptive neuro-fuzzy models for water quality index estimation. Water Res Manag 32(7):2227–2245. https://doi.org/10.1007/s11269-018-1915-7
Yu Z, Yang K, Luo Y, Shang C (2020) Spatial-temporal process simulation and prediction of chlorophyll-a concentration in Dianchi Lake based on wavelet analysis and long-short term memory network. J Hydrol 582. https://doi.org/10.1016/j.jhydrol.2019.124488
Zhang L, Jiang Z, He S, Duan J, Wang P, Zhou T (2022a) Study on water quality prediction of urban reservoir by coupled CEEMDAN decomposition and LSTM neural network model. Water Res Manag 36(10):3715–3735. https://doi.org/10.1007/s11269-022-03224-y
Zhang Q, Wang R, Qi Y, Wen F (2022b) A watershed water quality prediction model based on attention mechanism and Bi-LSTM. Environ Sci Pollut Res 29(50):75664–75680. https://doi.org/10.1007/s11356-022-21115-y
Zhang X, Chen X, Zheng G, Cao G (2023) Improved prediction of chlorophyll-a concentrations in reservoirs by GRU neural network based on particle swarm algorithm optimized variational modal decomposition. Environ Res 221:115259. https://doi.org/10.1016/j.envres.2023.115259
Zhou Y (2020) Real-time probabilistic forecasting of river water quality under data missing situation: Deep learning plus post-processing techniques. J Hydrol 589. https://doi.org/10.1016/j.jhydrol.2020.125164
Zhou HY, Zhang SH, Peng JQ, Zhang S, Li JX, Xiong H, Zhang WC, Assoc Advancement Artificial I (2021) Informer: Beyond efficient transformer for long sequence time-series forecasting. AAAI Conf Artif Intell 1:11106–11115. https://doi.org/10.48550/arXiv.2012.07436
Zou Q, Xiong Q, Li Q, Yi H, Yu Y, Wu C (2020) A water quality prediction method based on the multi-time scale bidirectional long short-term memory network. Environ Sci Pollut Res 27(14):16853–16864. https://doi.org/10.1007/s11356-020-08087-7
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This work was supported by Tianjin Water Conservancy Engineering Group Co., Ltd (grant number 2022GZX-0105).
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Qingqing Zhang: Concept, Design, Writing, Visualization, Reviewing and Editing. Xue-yi You: Supervision, Reviewing.
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Zhang, Q., You, Xy. Recent Advances in Surface Water Quality Prediction Using Artificial Intelligence Models. Water Resour Manage 38, 235–250 (2024). https://doi.org/10.1007/s11269-023-03666-y
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DOI: https://doi.org/10.1007/s11269-023-03666-y