River/stream water temperature forecasting using artifcial intelligence models: a systematic review

Zhu, Senlin; Piotrowski, Adam P.

doi:10.1007/s11600-020-00480-7

Artykuł - szczegóły

Tytuł artykułu

River/stream water temperature forecasting using artifcial intelligence models: a systematic review

Autorzy

Zhu Senlin , Piotrowski Adam P.

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-020-00480-7

Warianty tytułu

Języki publikacji

Abstrakty

Water temperature is one of the most important indicators of aquatic system, and accurate forecasting of water temperature is crucial for rivers. It is a complex process to accurately predict stream water temperature as it is impacted by a lot of factors (e.g., meteorological, hydrological, and morphological parameters). In recent years, with the development of computational capacity and artifcial intelligence (AI), AI models have been gradually applied for river water temperature (RWT) forecasting. The current survey aims to provide a systematic review of the AI applications for modeling RWT. The review is to show the progression of advances in AI models. The pros and cons of the established AI models are discussed in detail. Overall, this research will provide references for hydrologists and water resources engineers and planners to better forecast RWT, which will beneft river ecosystem management.

Słowa kluczowe

temperature forecasting river water artifcial intelligence model hybrid model review

prognozowanie temperatury woda rzeczna model sztucznej inteligencji model hybrydowy przegląd

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2020

Tom

Vol. 68, no. 5

Strony

1433--1442

Opis fizyczny

Bibliogr. 117 poz.

Twórcy

autor

Zhu Senlin

slzhu@nhri.cn

College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225127, China
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China

autor

Piotrowski Adam P.

Institute of Geophysics, Polish Academy of Sciences, Ks. Janusza 64, 01-452 Warsaw, Poland

Bibliografia

1. Afan HA, El-shafie A, Mohtar WHMW, Yaseen ZM (2016) Past, present and prospect of an Artificial Intelligence (AI) based model for sediment transport prediction. J Hydrol 541:902–913
2. Arismendi I, Safeeq M, Dunham JB, Johnson SL (2014) Can air temperature be used to project influences of climate change on stream temperature? Environ Res Lett 9:084015
3. Arora R, Tockner K, Venohor M (2016) Changing river temperatures in northern Germany: trends and drivers of change. Hydrol Processes 30:3084–3096
4. Atiquzzaman M, Kandasamy J (2016) Prediction of hydrological time-series using extreme learning machine. J Hydroinform 18(2):345–353
5. Benyahya L, Caissie D, St-Hilaire A, Ouarda TBMJ, Bobée B (2007) A review of statistical water temperature models. Can Water Resources J 32(3):179–192
6. Bromhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2:321–355
7. Bui QT, Nguyen QH, Nguyen XL, Pham VD, Nguyen HD, Pham VM (2020) Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping. J Hydrol 581:124379
8. Bui TD, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 45:199–211
9. Caissie D, El-Jabi N, St-Hilaire A (1998) Stochastic modelling of water temperatures in a small stream using air to water relations. Can J Civil Eng 25(2):250–260
10. Caissie D, El-Jabi N, Satish MG (2001) Modelling of maximum daily water temperatures in a small stream using air temperatures. J Hydrol 251(1–2):14–28
11. Caissie D, Thistle ME, Benyahya L (2017) River temperature forecasting: case study for Little Southwest Miramichi River (New Brunswick, Canada). Hydrol Sci J 62(5):683–697
12. Chandwani V, Vyas SK, Agrawal V, Sharma G (2015) Soft computing approach for rainfall-runoff modelling: a review. Aquatic Procedia 4:1054–1061
13. Chenard J, Caissie D (2008) Stream temperature modelling using artificial neural networks: application on Catamaran Brook, New Brunswick, Canada. Hydrol Processes 22:3361–3372
14. Cluis DA (1972) Relationship between stream water temperature and ambient air temperature. Hydrol Res 3(2):65–71
15. Cole JC, Maloney KO, Schmid M, McKenna JE Jr (2014) Developing and testing temperature models for regulated systems: a case study on the Upper Delaware River. J Hydrol 519:588–598
16. Crisp DT, Howson G (1982) Effect of air temperature upon mean water temperature in streams in the north Pennines and English Lake District. Freshw Biol 12(4):359–367
17. Daigle A, St-Hilaire A, Ouellet V, Corriveau J, Ouarda TBMJ, Bilodeau L (2009) Diagnostic study and modeling of the annual positive water temperature onset. J Hydrol 370:29–38
18. Daigle A, Ouarda TBMJ, Bilodeau L (2010) Comparison of parametric and non-parametric estimations of the annual date of positive water temperature onset. J Hydrol 390:75–84
19. DeWeber JT, Wagner T (2014) A regional neural network ensemble for predicting mean daily river water temperature. J Hydrol 517:187–200
20. Du X, Shrestha NK, Wang J (2019) Assessing climate change impacts on stream temperature in the Athabasca River basin using SWAT equilibrium temperature model and ist potential impacts on stream ecosystem. Sci Total Environ 650(2):1872–1881
21. Dugdale SJ, Hannah DM, Malcolm IA (2017) River temperature modelling: a review of process-based approaches and future directions. Earth-Sci Rev 175:97–113
22. Durbin R, Rumelhart DE (1989) Product Units: a computationally powerful and biologically plausible extension to backpropagation networks. Neural Comput 1:133–142
23. Erickson TR, Stefan HG (2000) Linear air/water temperature correlations for streams during open water periods. J Hydrol Eng 5(3):317–321
24. Faruk DO (2010) A hybrid neural network and ARIMA model for water quality time series prediction. Eng Appl Artificial Intell 23:586–594
25. Foreman MGG, Lee DK, Morrison J, Macdonald S, Barnes D, Williams IV (2001) Simulations and retrospective analyses of Fraser watershed flows and temperatures. Atmos Ocean 39(2):89–105
26. Goodfellow I, Bengio Y, Courville A (2016) Deep Learning. MIT Press, Cambridge
27. Graf R, Zhu S, Sivakumar B (2019) Forecasting river water temperature time series using a wavelet–neural network hybrid modelling approach. J Hydrol 578:124115
28. Grbic R, Kurtagic D, Sliškovic D (2013) Stream water temperature prediction based on Gaussian process regression. Expert Syst Appl 40:7407–7414
29. Gualtieri C, Gualtieri P, Doria GP (2002) Dimensional analysis of reaeration rate in streams. J Environ Eng 128(1):12–18
30. Guimarães Santos CA, da Silva GBL (2014) Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models. Hydrol Sci J 59(2):312–324
31. Hadzima-Nyarko M, Rabi A, Šperac M (2014) Implementation of artificial neural networks in modeling the water-air temperature relationship of the River Drava. Water Resources Manag 28(5):1379–1394
32. Haykin S (1999) Neural networks a comprehensive foundation. Prentice Hall, Upper Saddle River
33. Hebert C, Caissie D, Satish MG, El-Jabi N (2014) Modeling of hourly river water temperatures using artificial neural networks. Water Qual Res J Can 49(2):144–162
34. Heddam S (2016) New modelling strategy based on radial basis function neural network (RBFNN) for predicting dissolved oxygen concentration using the components of the Gregorian calendar as inputs: case study of Clackamas River, Oregon, USA. Model Earth Syst Environ 2:1–5
35. Heddam S, Kisi O (2017) Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors. Environ Sci Pollut Res 24:16702–16724
36. Holman D, Sridharan M, Gowda P, Porter D, Marek T, Howell T, Moorhead J (2014) Gaussian process models for reference ET estimation from alternative meteorological data sources. J Hydrol 517:28–35
37. Hong YST (2012) Dynamic nonlinear state-space model with a neural network via improved sequential learning algorithm for an online real-time hydrological modeling. J Hydrol 468–469:11–21
38. Hong YST, Bhamidimarri R (2012) Dynamic neuro-fuzzy local modeling system with a nonlinear feature extraction for the online adaptive warning system of river temperature affected by waste cooling water discharge. Stochastic Environ Res Risk Assess 26:947–960
39. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366
40. Hu R, Fang F, Pain CC, Navon IM (2019) Rapid spatio-temporal flood prediction and uncertainty quantification using a deep learning method. J Hydrol 575:911–920
41. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501
42. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference systems. IEEE Trans Syst Man Cybern 23:665–685
43. Jeong DI, Daigle A, St-Hilaire A (2013) Development of a stochastic water temperature model and projection of future water temperature and extreme events in the Ouelle River basin in Quebec, Canada. River Res Appl 29:805–821
44. Jeong K, Lee J, Lee KY, Kim B (2016) Artificial neural network-based real time water temperature prediction in the Soyang River. Trans Korean Institute Electrical Eng 65(12):2084–2093
45. Kleiber W, Katz RW, Rajagopalan B (2012) Daily spatiotemporal precipitation simulation using latent and transformed Gaussian processes. Water Resources Res 48(1):W01523
46. Knouft JH, Ficklin DL (2017) The potential impacts of climate change on biodiversity in flowing freshwater systems. Ann Rev Ecol Evolut Syst 48:111–133
47. Koch H, Grunewald U (2010) Regression models for daily stream temperature simulation: case studies for the river Elbe, Germany. Hydrol Process 24:3826–3836
48. Kothandaraman V (1971) Analysis of water temperature variations in large rivers. J Sanitary Eng Division 97(1):19–31
49. Kurnaz S, Cetin O, Kaynak O (2010) Adaptive neuro-fuzzy inference system based autonomous flight control of unmanned air vehicles. Expert Syst Appl 37(2):1229–1234
50. Laanya F, St-Hilaire A, Gloaguen E (2017) Water temperature modelling: comparison between the generalized additive model, logistic, residuals regression and linear regression models. Hydrol Sci J 62(7):1078–1093
51. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444
52. Lee T, Shin JY, Kim JS, Singh VP (2020) Stochastic simulation on reproducing long-term memory of hydroclimatological variables using deep learning model. J Hydrol 582:124540
53. Lessard JL, Hayes DB (2003) Effects of elevated water temperature on fish and macroinvertebrate communities below small dams. River Res Appl 19(7):721–732
54. Letcher BH, Hocking DJ, O’Neil K, Whiteley AR, Nislow KH, O’Donnell MJ (2016) A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags. PeerJ 4:e1727
55. Levenberg K (1944) A method for the solution of certain problems in least squares. Quart Appl Math 5:164–168
56. Liu H, Sun S, Zheng T, Li G (2018a) Prediction of water temperature regulation for spawning sites at downstream of hydropower station by artificial neural network method. Trans Chin Soc Agricult Eng 34(4):185–191
57. Liu D, Xu Y, Guo S, Xiong L, Liu P, Zhao Q (2018b) Stream temperature response to climate change and water diversion activities. Stochastic Environ Res Risk Assess 32:1397–1413
58. Lu H, Ma X (2020) Hybrid decision tree-based machine learning models for short-term water quality prediction. Chemosphere 249:126169
59. Matsumoto K, Hashioka T, Yamanaka Y (2007) Effect of temperature-dependent organic carbon decay on atmospheric pCO2. J Geophys Res Biogeosci 112(2):G02007
60. Mohandes M, Rehman S, Rahman SM (2011) Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS). Appl Energy 88(11):4024–4032
61. Mohseni O, Stefan HG, Erickson TR (1998) A nonlinear regression model for weekly stream temperatures. Water Resources Res 34(10):2685–2692
62. Nourani V, Alami MT, Aminfar MH (2009) A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Eng Appl Artif Intell 22(3):466–472
63. Nourani V, Hosseini Baghanam A, Adamowski J, Kisi O (2014) Applications of hybrid wavelet-Artificial Intelligence models in hydrology: a review. J Hydrol 514:358–377
64. Petrović DV, Tanasijević M, Milić V, Lilić N, Stojadinović S, Svrkota I (2014) Risk assessment model of mining equipment failure based on fuzzy logic. Expert Syst Appl 41(18):8157–8164
65. Pilgrim JM, Fang X, Stefan HG (1998) Stream temperature correlations with air temperatures in Minnesota: implications for climate warming. J Am Water Resources Assoc 34(5):1109–1121
66. Piotrowski AP, Napiorkowski JJ (2019) Simple modifications of the nonlinear regression stream temperature model for daily data. J Hydrol 572:308–328
67. Piotrowski AP, Napiorkowski MJ, Kalinowska M, Napiorkowski JJ, Osuch M (2016) Are evolutionary algorithms effective in calibrating different artificial neural network types for streamwater temperature prediction? Water Resources Manage 30(3):1217–1237
68. Piotrowski AP, Napiorkowski MJ, Napiorkowski JJ, Osuch M (2015) Comparing various artificial neural network types for water temperature prediction in rivers. J Hydrol 529:302–315
69. Piotrowski AP, Napiorkowski MJ, Piotrowska AE (2020) Impact of deep learning-based dropout on shallow neural networks applied to stream temperature modelling. Earth-Sci Rev 201:103076
70. Piotrowski AP, Osuch M, Napiorkowski MJ, Rowinski PM, Napiorkowski JJ (2014) Comparing large number of metaheuristics for artificial neural networks training to predict water temperature in a natural river. Comput Geosci 64:136–151
71. Poul AK, Shourian M, Ebrahim H (2019) A comparative study of MLR, KNN, ANN and ANFIS models with wavelet transform in monthly stream flow prediction. Water Resources Manag 33:2907–2923
72. Qiu R, Wang Y, Wang D, Qiu W, Wu J, Tao Y (2020) Water temperature forecasting based on modified artificial neural network methods: two cases of the Yangtze River. Sci Total Environ 737:139729
73. Quilty J, Adamowski J (2018) Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real-world applications with best practices and a new forecasting framework. J Hydrol 563:336–353
74. Quinn JM, Steele GL, Hickey CW, Vickers ML (1994) Upper thermal tolerances of twelve New Zealand stream invertebrate species. N Z J Mar Freshw Res 28(4):391–397
75. Rabi A, Hadzima-Nyarko M, Šperac M (2015) Modelling river temperature from air temperature: case of the River Drava (Croatia). Hydrol Sci J 60(9):1490–1507
76. Rehana S (2019) River water temperature modelling under climate change using support vector regression. In Hydrology in a Changing World. Springer, Cham, pp 171–183.
77. Rezaie-Balf M, Kisi O (2017) New formulation for forecasting streamflow: evolutionary polynomial regression vs. extreme learning machine. Hydrol Res 49(3):939–953.
78. Razavi Termeh SV, Kornejady A, Pourghasemi HR, Keesstra S (2018) Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Sci Total Environ 615:438–451
79. Sahoo GB, Schladow SG, Reuter JE (2009) Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models. J Hydrol 378(3–4):325–342
80. Sang YF (2013) A review on the applications of wavelet transform in hydrology time series analysis. Atmos Res 122:8–15
81. Schaffer C (1993) Overfitting avoidance as bias. Mach Learn 10:153–178
82. Shen C (2018) A transdisciplinary review of deep learning research and its relevance for water resources scientists. Water Resources Res 54(11):8558–8593
83. Shen C, Laloy E, Elshorbagy A, Albert A, Bales J, Chang FJ, Ganguly S, Hsu KL, Kifer D, Fang Z, Fang K, Li D, Li X, Tsai WP (2018) HESS opinions: incubating deep-learning-powered hydrologic science advances as a community. Hydrol Earth Syst Sci 22:5639–5656
84. Sinokrot BA, Stefan HG (1993) Stream temperature dynamics: measurements and modeling. Water Resources Res 29(7):2299–2312
85. Sivri N, Kilic N, Ucan ON (2007) Estimation of stream temperature in Firtina Creek (Rize-Turkiye) using artificial neural network model. J Environ Biol 28(1):67–72
86. Sivri N, Ozcan HK, Ucan ON, Akincilar O (2009) Estimation of stream temperature in Degirmendere River (Trabzon-Turkey) using artificial neural network model. Turkish J Fish Aquat Sci 9:145–150
87. Smith K (1981) The prediction of river water temperatures. Hydrol Sci J 26(1):19–32
88. Sohrabi MM, Benjankar R, Tonina D, Wenger SJ, Isaak DJ (2017) Estimation of daily stream water temperatures with a Bayesian regression approach. Hydrol Processes 31:1719–1733
89. Soto B (2016) Assessment of trends in stream temperatures in the north of the Iberian peninsula using a nonlinear regression model for the period 1950–2013. River Res Appl 32:1355–1364
90. Soto B (2018) Climate-induced changes in river water temperature in North Iberian Peninsula. Theor Appl Climatol 133:101–112
91. Stefan HG, Preud’homme EB, (1993) Stream temperature estimation from air temperature. J Am Water Resources Assoc 29(1):27–45
92. Stewart JS, Westenbroek SM, Mitro MG, Lyons JD, Kammel LE, Buchwald CA (2014) A model for evaluating stream temperature response to climate change in Wisconsin. USGS Report 2014–5186.
93. Sun AY, Wang D, Xu X (2014) Monthly streamflow forecasting using Gaussian process Regression. J Hydrol 511:72–81
94. Sun AY, Scanlon BR, Zhang Z, Walling D, Bhanja SN, Mukherjee A, Zhong Z (2019) Combining physically based modeling and deep learning for fusing GRACE satellite data: can we learn from mismatch? Water Resources Res 5:1179–1195
95. Tao W, Kailin Y, Yongxin G (2008) Application of artificial neural networks to forecasting ice conditions of the Yellow River in the Inner Mongolia reach. J Hydrol Eng ASCE 13(9):811–816
96. Temizyurek M, Dadaser-Celik F (2018) Modelling the effects of meteorological parameters on water temperature using artificial neural networks. Water Sci Technol 77(6):1724–1733
97. Toffolon M, Piccolroaz S (2015) A hybrid model for river water temperature as a function of air temperature and discharge. Environ Res Lett 10(11):114011
98. Voza D, Vukovic M (2018) The assessment and prediction of temporal variations in surface water quality—a case study. Environ Monit Assess 190:434
99. van Vliet MTH, Ludwig F, Zwolsman JJG, Weedon GP, Kabat P (2011) Global river temperatures and sensitivity to atmospheric warming and changes in river flow. Water Resources Res 47:W02544
100. Watts G, Battarbee RW, Bloomfield JP, Crossman J, Daccache A, Durance I, Elliott JA, Garner G, Hannaford J, Hannah DM, Hess T, Jackson CR, Kay AL, Kernan M, Knox J, Mackay J, Monteith DT, Ormerod SJ, Rance J, Stuart ME, Wade AJ, Wade SD, Weatherhead K, Whitehead PG, Wilby RL (2015) Climate change and water in the UK—past changes and future prospects. Progress Phys Geogr 39(1):6–28
101. Webb BW, Clack PD, Walling DE (2003) Water-air temperature relationships in a Devon river system and the role of flow. Hydrol Process 17(15):3069–3084
102. Webb BW, Hannah DM, Moore RD, Brown LE, Nobilis F (2008) Recent advances in stream and river temperature research. Hydrol Process 22:902–918
103. Wright SA, Anderson CR, Voichick N (2009) A simplified water temperature model for the Colorado River below Glen Canyon Dam. River Res Appl 25(6):675–686
104. Yaseen ZM, Allawi MF, Yousif AA, Jaafar O, Hamzah FM, El-Shafie A (2018) Non-tuned machine learning approach for hydrological time series forecasting. Neural Comput Appl 30(5):1479–1491
105. Yaseen ZM, Sulaiman SO, Deo RC, Chau KW (2019) An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction. J Hydrol 569:387–408
106. Zadeh LA (1988) Fuzzy logic. Computer 21(4):83–93
107. Zhai P, Williams ED (2012) Analyzing consumer acceptance of photovoltaics (PV) using fuzzy logic model. Renewable Energy 41:350–357
108. Zhang S, Wang Y, He W, Wu M, Xing M, Yang J, Gao N, Pan M (2014) Impacts of temperature and nitrifying community on nitrification kinetics in a moving-bed biofilm reactor treating polluted raw water. Chem Eng J 236:242–250
109. Zhu S, Bonacci O, Oskoruš D, Hadzima-Nyarko M, Wu S (2019a) Long term variations of river temperature and the influence of air temperature and river discharge: case study of Kupa River watershed in Croatia. J Hydrol Hydromech 67(4):305–313
110. Zhu S, Hadzima-Nyarko M, Gao A, Wang F, Wu J, Wu S (2019b) Two hybrid data-driven models for modeling water-air temperature relationship in rivers. Environ Sci Pollut Res 26:12622–12630
111. Zhu S, Heddam S (2019) Modelling of maximum daily water temperature for streams: optimally pruned extreme learning machine (OPELM) versus radial basis function neural networks (RBFNN). Environ Process 6(3):789–804
112. Zhu S, Heddam S, Nyarko EK, Hadzima-Nyarko M, Piccolroaz S, Wu S (2019c) Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models. Environ Sci Pollut Res 26:402–420
113. Zhu S, Heddam S, Wu S, Dai J, Jia B (2019d) Extreme learning machine-based prediction of daily water temperature for rivers. Environ Earth Sci 78(6):202
114. Zhu S, Hrnjica B, Ptak M, Choiński A, Sivakumar B (2020a) Forecasting of water level in multiple temperate lakes using machine learning models. J Hydrol 585:124819
115. Zhu S, Nyarko EK, Hadzima-Nyarko M (2018) Modelling daily water temperature from air temperature for the Missouri River. PeerJ 6:e4894
116. Zhu S, Nyarko EK, Hadzima-Nyarko M, Heddam S, Wu S (2019e) Assessing the performance of a suite of machine learning models for daily river water temperature prediction. PeerJ 7:e7065
117. Zhu S, Ptak M, Yaseen ZM, Dai J, Sivakumar B (2020b) Forecasting surface water temperature in lakes: a comparison of approaches. J Hydrol 585:124809

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