Estimación de la temperatura del agua: un relevamiento de modelos estadísticos para aplicación en IOT y Tanques de Acuicultura
DOI:
https://doi.org/10.33448/rsd-v12i4.41142Palabras clave:
Modelos estadísticos; Temperatura del agua; Acuicultura.Resumen
La temperatura del agua es una propiedad física importante para la salud de los ecosistemas acuáticos, ya que afecta la concentración de saturación de oxígeno disuelto en el agua y altera las reacciones químicas y biológicas que pueden amenazar el metabolismo, la reproducción, el crecimiento y la supervivencia de las especies. Por lo tanto, para garantizar el cumplimiento del agua para las producciones acuícolas u otras áreas dependientes de la temperatura, es esencial el monitoreo constante del agua. Existen varios dispositivos que permiten esta medición, sin embargo, no están presentes en todos los lugares que tienen esta necesidad. Alternativamente, la estimación de la temperatura del agua se puede aplicar en estos entornos. El objetivo de este estudio es realizar una revisión sistemática de la literatura que presente un mapeo de los modelos estadísticos utilizados para estimar la temperatura del agua en ríos. Se han utilizado diversos modelos estadísticos para este fin en varias partes del mundo, incluyendo Brasil. Este mapeo tiene como objetivo identificar cuáles modelos se han utilizado, así como realizar comparaciones y análisis críticos de los usos y evaluaciones de estos modelos.
Citas
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
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2023 Jheklos Gomes da Silva; Ricardo André Cavalcante de Souza; Obionor de Oliveira Nobrega
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Los autores que publican en esta revista concuerdan con los siguientes términos:
1) Los autores mantienen los derechos de autor y conceden a la revista el derecho de primera publicación, con el trabajo simultáneamente licenciado bajo la Licencia Creative Commons Attribution que permite el compartir el trabajo con reconocimiento de la autoría y publicación inicial en esta revista.
2) Los autores tienen autorización para asumir contratos adicionales por separado, para distribución no exclusiva de la versión del trabajo publicada en esta revista (por ejemplo, publicar en repositorio institucional o como capítulo de libro), con reconocimiento de autoría y publicación inicial en esta revista.
3) Los autores tienen permiso y son estimulados a publicar y distribuir su trabajo en línea (por ejemplo, en repositorios institucionales o en su página personal) a cualquier punto antes o durante el proceso editorial, ya que esto puede generar cambios productivos, así como aumentar el impacto y la cita del trabajo publicado.
