Estimation of water temperature: a survey of statistical models for application in IOT and Aquaculture Tanks
Keywords:Statistical models; Water temperature; Aquaculture.
Water temperature is an important physical property for the health of aquatic ecosystems because it affects the concentration of oxygen saturation dissolved in the water and alters chemical and biological reactions that can cause species to have their metabolism, reproduction, growth and survival threatened. Therefore, to ensure that water is compliant for aquaculture production or other area that depend of the temperature, the constant water monitoring is essential. There are several devices that allow this measurement, however, they are not present in all places that have this need. Alternatively, water temperature estimation can be applied in these environments. The objective of this study is to conduct a systematic literature mapping that presents a review of statistical models used to estimate water temperature in rivers. Several statistical models have been used for this purpose in various parts of the world, including Brazil. This mapping aims to identify which models have been used, as well as to perform comparisons and critical analysis of the uses and evaluations of these models.
Ali, S., Mishra, P. K., Islam, A., & Alam, N. M. (2016). Simulation of water temperature in a small pond using parametric statistical models: implications of climate warming. Journal of Environmental Engineering, 142(3), 04015085. https://ascelibrary.org/doi/abs/10.1061/(ASCE)EE.1943-7870.0001050
Ahmadi‐Nedushan, B., St‐Hilaire, A., Ouarda, T. B., Bilodeau, L., Robichaud, E., Thiémonge, N., & Bobée, B. (2007). Predicting river water temperatures using stochastic models: case study of the Moisie River (Québec, Canada). Hydrological Processes: An International Journal, 21(1), 21-34. https://onlinelibrary.wiley.com/doi/abs/10.1002/hyp.6353
Antonopoulos, V. Z., & Gianniou, S. K. (2003). Simulation of water temperature and dissolved oxygen distribution in Lake Vegoritis, Greece. Ecological modelling, 160(1-2), 39-53. https://www.sciencedirect.com/science/article/abs/pii/S0304380002002867
Benyahya L., Caissie D., St-Hilaire A., Ouarda T.B.M.J. and Bobee B., 2007a. A review of statistical water temperature models. Can. Water Resources J., 32, 179–192. https://www.tandfonline.com/doi/abs/10.4296/cwrj3203179
Caldwell, J., Rajagopalan, B., & Danner, E. (2015). Statistical modeling of daily water temperature attributes on the Sacramento River. Journal of Hydrologic Engineering, 20(5), 04014065. https://ascelibrary.org/doi/abs/10.1061/(asce)he.1943-5584.0001023
Caldwell, R. J., Gangopadhyay, S., Bountry, J., Lai, Y., & Elsner, M. M. (2013). Statistical modeling of daily and subdaily stream temperatures: Application to the Methow River Basin, Washington. Water Resources Research, 49(7), 4346-4361. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/wrcr.20353
Cordovil, v. R. D. S., & Francelin, m. M. (2018). Organização e representações: uso de mapa mental e mapa conceitual. Xix encontro nacional de pesquisa em ciência da informação (xix enancib); xix encontro nacional de pesquisa em ciência da informação (xix enancib), 24(2). https://brapci.inf.br/index.php/res/v/103035
Chenard J.F. and Caissie D., 2008. Stream temperature modelling using artificial neural networks: application on Catamaran Brook, New Brunswick, Canada. Hydrol. Process., 22, 336. https://onlinelibrary.wiley.com/doi/abs/10.1002/hyp.6928
Colombo, G. T., & Mannich, M. (2019). Estimativa da temperatura da água em rios utilizando a média móvel da temperatura do ar. Proceedings of the XXIII SBRH 2019. https://s3-sa-east-1.amazonaws.com/abrh/Eventos/Trabalhos/107/XXIII-SBRH0387-1-20190502-111941.pdf
de Cara, B. E. D., Luiz, A. J. B., & Neves, M. C. (2013). Método para expansão de uma série temporal de temperatura da água a partir de dados do sistema automático de monitoramento de variáveis ambientais (SIMA): aplicação em aquicultura no reservatório de Furnas. https://www.alice.cnptia.embrapa.br/handle/doc/963340
Frascá-Scorvo, C. M., Carneiro, D. J., & Malheiros, E. B. (2001). Comportamento alimentar do matrinxã (Brycon cephalus) no período de temperaturas mais baixas. Boletim do Instituto de pesca, 27(1), 1-5.
Ferchichi, H., St-Hilaire, A., Ouarda, T. B., & Lévesque, B. (2022). Prediction of coastal water temperature using statistical models. Estuaries and Coasts, 45(7), 1909-1927. https://link.springer.com/article/10.1007/s12237-022-01070-0
Harvey, R., Lye, L., Khan, A., & Paterson, R. (2011). The influence of air temperature on water temperature and the concentration of dissolved oxygen in Newfoundland Rivers. Canadian Water Resources Journal, 36(2), 171-192. https://doi.org/10.4296/cwrj3602849
Hague, M. J., & Patterson, D. A. (2014). Evaluation of statistical river temperature forecast models for fisheries management. North American Journal of Fisheries Management, 34(1), 132-146. https://www.tandfonline.com/doi/abs/10.1080/02755947.2013.847879
Heddam, S., Ptak, M., & Zhu, S. (2020). Modelling of daily lake surface water temperature from air temperature: Extremely randomized trees (ERT) versus Air2Water, MARS, M5Tree, RF and MLPNN. Journal of Hydrology, 588, 125130. https://www.sciencedirect.com/science/article/abs/pii/S0022169420305904
Jeppesen, E., & Iversen, T. M. (1987). Two simple models for estimating daily mean water temperatures and diel variations in a Danish low gradient stream. Oikos, 149-155. https://www.jstor.org/stable/3566020
Jiang, D., Xu, Y., Lu, Y., Gao, J., & Wang, K. (2022). Forecasting Water Temperature in Cascade Reservoir Operation-Influenced River with Machine Learning Models. Water, 14(14), 2146. https://www.mdpi.com/2073-4441/14/14/2146?utm_campaign=releaseissue_waterutm_medium=emailutm_source=releaseissueutm_term=doilink54
Larnier, K., Roux, H., Dartus, D., & Croze, O. (2010). Water temperature modeling in the Garonne River (France). Knowledge and Management of Aquatic Ecosystems, (398), 04. https://www.kmae-journal.org/articles/kmae/abs/2010/03/kmae100021/kmae100021.html
Laanaya, F., St-Hilaire, A., & Gloaguen, E. (2017). Water temperature modelling: comparison between the generalized additive model, logistic, residuals regression and linear regression models. Hydrological sciences journal, 62(7), 1078-1093. https://www.tandfonline.com/doi/full/10.1080/02626667.2016.1246799
Letcher, B. H., Hocking, D. J., O’Neil, K., Whiteley, A. R., Nislow, K. H., & O’Donnell, M. J. (2016). A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags. PeerJ, 4, e1727. https://peerj.com/articles/1727/
Liu, W. C., & Chen, W. B. (2012). Prediction of water temperature in a subtropical subalpine lake using an artificial neural network and three-dimensional circulation models. Computers & Geosciences, 45, 13-25. https://www.sciencedirect.com/science/article/pii/S0098300412000982
Mohseni, O., Stefan, H. G., and Erickson, T. R. (1998). “A nonlinear regression model for weekly stream temperature.” Water Resour. Res., 34(10), 2685–2692. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/98WR01877
Mohr, S., Drainas, K., & Geist, J. (2021, December). Assessment of Neural Networks for Stream-Water-Temperature Prediction. In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 891-896). IEEE. https://ieeexplore.ieee.org/abstract/document/9680252
Moura, G. D. S., Oliveira, M. G. A., Lanna, E. T. A., Maciel Júnior, A., & Maciel, C. M. R. R. (2007). Desempenho e atividade de amilase em tilápias-do-nilo submetidas a diferentes temperaturas. Pesquisa Agropecuária Brasileira, 42, 1609-1615.
McGrath, E. O., Neumann, N. N., & Nichol, C. F. (2017). A statistical model for managing water temperature in streams with anthropogenic influences. River Research and Applications, 33(1), 123-134. https://onlinelibrary.wiley.com/doi/abs/10.1002/rra.3057
Petersen, K.; Feldt, R.; Mujtaba, S.; Mattsson, M. (2008) Systematic Mapping Studies in Software Engineering. 12th International Conference on Evaluation and Assessment in Software Engineering (EASE). University of Bari, Italy. https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/EASE2008.8
Piccolroaz, S., Calamita, E., Majone, B., Gallice, A., Siviglia, A., & Toffolon, M. (2016). Prediction of river water temperature: a comparison between a new family of hybrid models and statistical approaches. Hydrological Processes, 30(21), 3901-3917. https://onlinelibrary.wiley.com/doi/abs/10.1002/hyp.10913
Piccolroaz, S., Toffolon, M., & Majone, B. (2013). A simple lumped model to convert air temperature into surface water temperature in lakes. Hydrology and earth system sciences, 17(8), 3323-3338. https://hess.copernicus.org/articles/17/3323/2013/
Pike, A., Danner, E., Boughton, D., Melton, F., Nemani, R., Rajagopalan, B., & Lindley, S. (2013). Forecasting river temperatures in real time using a stochastic dynamics approach. Water Resources Research, 49(9), 5168-5182. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/wrcr.20389
Rabi, A., Hadzima-Nyarko, M., & Šperac, M. (2015). Modelling river temperature from air temperature: case of the River Drava (Croatia). Hydrological sciences journal, 60(9), 1490-1507. https://www.tandfonline.com/doi/full/10.1080/02626667.2014.914215
St‐Hilaire, A., Ouarda, T. B., Bargaoui, Z., Daigle, A., & Bilodeau, L. (2012). Daily river water temperature forecast model with ak‐nearest neighbour approach. Hydrological Processes, 26(9), 1302-1310. https://onlinelibrary.wiley.com/doi/abs/10.1002/hyp.8216
Saeed, S., Honeyeh, K., Ozgur, K., & Wen-Cheng, L. (2016). Water temperature prediction in a subtropical subalpine lake using soft computing techniques. Earth Sciences Research Journal, 20(2), 1-11. http://www.scielo.org.co/scielo.php?pid=S1794-61902016000200005&script=sci_arttext&tlng=en
Sahoo, G. B., Schladow, S. G., & Reuter, J. E. (2009). Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models. Journal of hydrology, 378(3-4), 325-342. https://www.sciencedirect.com/science/article/abs/pii/S0022169409006118
Sun, M., Chen, J., & Li, D. (2012, May). Water temperature prediction in sea cucumber aquaculture ponds by RBF neural network model. In 2012 International Conference on Systems and Informatics (ICSAI2012) (pp. 1154-1159). IEEE. https://ieeexplore.ieee.org/abstract/document/6223239
Tasnim, B., Jamily, J. A., Fang, X., Zhou, Y., & Hayworth, J. S. (2021). Simulating diurnal variations of water temperature and dissolved oxygen in shallow Minnesota lakes. Water, 13(14), 1980. https://www.mdpi.com/2073-4441/13/14/1980
Toffolon, M., & Piccolroaz, S. (2015). A hybrid model for river water temperature as a function of air temperature and discharge. Environmental Research Letters, 10(11), 114011. https://iopscience.iop.org/article/10.1088/1748-9326/10/11/114011/meta
Wenxian, G., Hongxiang, W., Jianxin, X., & Wensheng, D. (2010, May). PSO-BP neural network model for predicting water temperature in the middle of the Yangtze river. In 2010 International Conference on Intelligent Computation Technology and Automation (Vol. 2, pp. 951-954). IEEE. https://ieeexplore.ieee.org/abstract/document/5522936
Yearsley, J. (2012). A grid‐based approach for simulating stream temperature. Water Resources Research, 48(3). https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011WR011515
Zhu, S., Ptak, M., Yaseen, Z. M., Dai, J., & Sivakumar, B. (2020). Forecasting surface water temperature in lakes: A comparison of approaches. Journal of Hydrology, 585, 124809. https://www.sciencedirect.com/science/article/abs/pii/S0022169420302699
Zhu, S., Nyarko, E. K., & Hadzima-Nyarko, M. (2018). Modelling daily water temperature from air temperature for the Missouri River. PeerJ, 6, e4894. https://peerj.com/articles/4894/
Zhu, S., Heddam, S., Wu, S., Dai, J., & Jia, B. (2019). Extreme learning machine-based prediction of daily water temperature for rivers. Environmental Earth Sciences, 78, 1-17. https://link.springer.com/article/10.1007/s12665-019-8202-7
Zhu, S., Heddam, S., Nyarko, E. K., Hadzima-Nyarko, M., Piccolroaz, S., & Wu, S. (2019). Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models. Environmental Science and Pollution Research, 26, 402-420. https://link.springer.com/article/10.1007/s11356-018-3650-2
How to Cite
Copyright (c) 2023 Jheklos Gomes da Silva; Ricardo André Cavalcante de Souza; Obionor de Oliveira Nobrega
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.